Relevant bibliographies by topics / Goal Abstraction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Goal Abstraction'

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Published: 12 April 2025

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Journal articles on the topic "Goal Abstraction"

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Lee, David S., and Oscar Ybarra. "Cultivating Effective Social Support Through Abstraction." Personality and Social Psychology Bulletin 43, no. 4 (January 31, 2017): 453–64. http://dx.doi.org/10.1177/0146167216688205.

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Social support, in theory, should promote individual goal-pursuit. However, a growing number of studies shows that receiving support can undermine goal-pursuit. Addressing this paradox, we investigated a novel idea of the effects of how people think about their social support on their goal-pursuit. Four experiments showed that participants who were led to think abstractly (vs. concretely) about their social support showed higher intent to pursue their goal (Studies 1-3) and worked harder toward their goal (Study 4). The benefits of abstracting one’s social support occurred over a variety of personal goals, support types, and support-providers, indicating the generalizability and robustness of our findings. These results demonstrate that how people think about their social support influences goal-pursuit and suggest ways in which support-recipients can maximize their social support.
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Matook, Sabine, and Hans van der Heijden. "Goal Abstraction, Goal Linkage Dependency, and Perceived Utilitarian Value of Information Systems." Journal of Organizational and End User Computing 25, no. 2 (April 2013): 41–58. http://dx.doi.org/10.4018/joeuc.2013040103.

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Prior research has shown that the utilitarian value of an information system influences user acceptance because of the way the system helps to achieve certain user goals. It is less recognized in the literature that users have multiple goals that influence a user’s perception regarding the information system’s utilitarian value. This paper extends this body of knowledge by incorporating different types of goals into a theoretical framework. Building on means-end chain theory, two goal characteristics are identified, goal abstraction and goal linkage dependency, that both exert an influence on the utilitarian value of an information system. Findings from a qualitative and a quantitative study indicate that perceived utilitarian value changes as goal abstraction increases, and that goal linkage dependency influences the similarity of perceived utilitarian values across goals. Implications are important because they provide insight into potentially contradicting user evaluations of information systems.
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Abel, David. "A Theory of State Abstraction for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9876–77. http://dx.doi.org/10.1609/aaai.v33i01.33019876.

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Reinforcement learning presents a challenging problem: agents must generalize experiences, efficiently explore the world, and learn from feedback that is delayed and often sparse, all while making use of a limited computational budget. Abstraction is essential to all of these endeavors. Through abstraction, agents can form concise models of both their surroundings and behavior, supporting effective decision making in diverse and complex environments. To this end, the goal of my doctoral research is to characterize the role abstraction plays in reinforcement learning, with a focus on state abstraction. I offer three desiderata articulating what it means for a state abstraction to be useful, and introduce classes of state abstractions that provide a partial path toward satisfying these desiderata. Collectively, I develop theory for state abstractions that can 1) preserve near-optimal behavior, 2) be learned and computed efficiently, and 3) can lower the time or data needed to make effective decisions. I close by discussing extensions of these results to an information theoretic paradigm of abstraction, and an extension to hierarchical abstraction that enjoys the same desirable properties.
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Seipp, Jendrik, and Malte Helmert. "Counterexample-Guided Cartesian Abstraction Refinement for Classical Planning." Journal of Artificial Intelligence Research 62 (July 25, 2018): 535–77. http://dx.doi.org/10.1613/jair.1.11217.

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Counterexample-guided abstraction refinement (CEGAR) is a method for incrementally computing abstractions of transition systems. We propose a CEGAR algorithm for computing abstraction heuristics for optimal classical planning. Starting from a coarse abstraction of the planning task, we iteratively compute an optimal abstract solution, check if and why it fails for the concrete planning task and refine the abstraction so that the same failure cannot occur in future iterations. A key ingredient of our approach is a novel class of abstractions for classical planning tasks that admits efficient and very fine-grained refinement. Since a single abstraction usually cannot capture enough details of the planning task, we also introduce two methods for producing diverse sets of heuristics within this framework, one based on goal atoms, the other based on landmarks. In order to sum their heuristic estimates admissibly we introduce a new cost partitioning algorithm called saturated cost partitioning. We show that the resulting heuristics outperform other state-of-the-art abstraction heuristics in many benchmark domains.
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Surynek, Pavel. "Non-Refined Abstractions in Counterexample Guided Abstraction Refinement for Multi-Agent Path Finding (Extended Abstract)." Proceedings of the International Symposium on Combinatorial Search 17 (June 1, 2024): 287–88. http://dx.doi.org/10.1609/socs.v17i1.31584.

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Counterexample guided abstraction refinement (CEGAR) represents a powerful symbolic technique for various tasks such as model checking and reachability analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been applied for multi-agent path finding (MAPF), a problem where the task is to navigate agents from their start positions to given individual goal positions so that agents do not collide with each other. The recent CEGAR approach used the initial abstraction of the MAPF problem where collisions between agents were omitted and were eliminated in subsequent abstraction refinements. We propose in this work a novel CEGAR-style solver for MAPF based on SAT in which some abstractions are deliberately left non-refined. This adds the necessity to post-process the answers obtained from the underlying SAT solver as these answers slightly differ from the correct MAPF solutions. Non-refining however yields order-of-magnitude smaller SAT encodings than those of the previous approach and speeds up the overall solving process.
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Sriraman, Bharath. "Discovering Steiner Triple Systems through Problem Solving." Mathematics Teacher 97, no. 5 (May 2004): 320–26. http://dx.doi.org/10.5951/mt.97.5.0320.

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Principles and Standards for School Mathematics (NCTM 2000) calls for instructional programs that emphasize problem solving and that have the goal of helping students develop sophistication with such mathematical processes as representation, mathematical reasoning, abstraction, and generalization. In particular, the Problem Solving Standard suggests that teachers should choose problems that further the mathematical goals of the class. Problem solving can be viewed as a process through which teachers can help students think mathematically, which Schoenfeld (1985, 1992) defines as developing a mathematical point of view. It includes valuing the processes of representation and abstraction and having the predisposition to generalize them. In this article, I describe my attempt to implement problem solving as a teacher of ninthgrade algebra. I had two explicit goals in mind. The first goal was to use carefully chosen problemsolving situations as a setting for an extended mathematical investigation that leads to the discovery of Steiner triple systems. The second goal was to use problem-solving situations to help students think mathematically, that is, to construct representations and to engage in mathematical reasoning, abstraction, and generalization.
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Sriraman, Bharath. "Discovering Steiner Triple Systems through Problem Solving." Mathematics Teacher 97, no. 5 (May 2004): 320–26. http://dx.doi.org/10.5951/mt.97.5.0320.

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Principles and Standards for School Mathematics (NCTM 2000) calls for instructional programs that emphasize problem solving and that have the goal of helping students develop sophistication with such mathematical processes as representation, mathematical reasoning, abstraction, and generalization. In particular, the Problem Solving Standard suggests that teachers should choose problems that further the mathematical goals of the class. Problem solving can be viewed as a process through which teachers can help students think mathematically, which Schoenfeld (1985, 1992) defines as developing a mathematical point of view. It includes valuing the processes of representation and abstraction and having the predisposition to generalize them. In this article, I describe my attempt to implement problem solving as a teacher of ninthgrade algebra. I had two explicit goals in mind. The first goal was to use carefully chosen problemsolving situations as a setting for an extended mathematical investigation that leads to the discovery of Steiner triple systems. The second goal was to use problem-solving situations to help students think mathematically, that is, to construct representations and to engage in mathematical reasoning, abstraction, and generalization.
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Wientjes, Sven, and Clay B. Holroyd. "The successor representation subserves hierarchical abstraction for goal-directed behavior." PLOS Computational Biology 20, no. 2 (February 20, 2024): e1011312. http://dx.doi.org/10.1371/journal.pcbi.1011312.

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Humans have the ability to craft abstract, temporally extended and hierarchically organized plans. For instance, when considering how to make spaghetti for dinner, we typically concern ourselves with useful “subgoals” in the task, such as cutting onions, boiling pasta, and cooking a sauce, rather than particulars such as how many cuts to make to the onion, or exactly which muscles to contract. A core question is how such decomposition of a more abstract task into logical subtasks happens in the first place. Previous research has shown that humans are sensitive to a form of higher-order statistical learning named “community structure”. Community structure is a common feature of abstract tasks characterized by a logical ordering of subtasks. This structure can be captured by a model where humans learn predictions of upcoming events multiple steps into the future, discounting predictions of events further away in time. One such model is the “successor representation”, which has been argued to be useful for hierarchical abstraction. As of yet, no study has convincingly shown that this hierarchical abstraction can be put to use for goal-directed behavior. Here, we investigate whether participants utilize learned community structure to craft hierarchically informed action plans for goal-directed behavior. Participants were asked to search for paintings in a virtual museum, where the paintings were grouped together in “wings” representing community structure in the museum. We find that participants’ choices accord with the hierarchical structure of the museum and that their response times are best predicted by a successor representation. The degree to which the response times reflect the community structure of the museum correlates with several measures of performance, including the ability to craft temporally abstract action plans. These results suggest that successor representation learning subserves hierarchical abstractions relevant for goal-directed behavior.
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Calmet, Jacques, and Marvin Oliver Schneider. "Decision Making Modeled as a Theorem Proving Process." International Journal of Decision Support System Technology 4, no. 3 (July 2012): 1–11. http://dx.doi.org/10.4018/jdsst.2012070101.

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The authors introduce a theoretical framework enabling to process decisions making along some of the lines and methodologies used to mechanize mathematics and more specifically to mechanize the proofs of theorems. An underlying goal of Decision Support Systems is to trust the decision that is designed. This is also the main goal of their framework. Indeed, the proof of a theorem is always trustworthy. By analogy, this implies that a decision validated through theorem proving methodologies brings trust. To reach such a goal the authors have to rely on a series of abstractions enabling to process all of the knowledge involved in decision making. They deal with an Agent Oriented Abstraction for Multiagent Systems, Object Mechanized Computational Systems, Abstraction Based Information Technology, Virtual Knowledge Communities, topological specification of knowledge bases using Logical Fibering. This approach considers some underlying hypothesis such that knowledge is at the heart of any decision making and that trust transcends the concept of belief. This introduces methodologies from Artificial Intelligence. Another overall goal is to build tools using advanced mathematics for users without specific mathematical knowledge.
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Seipp, Jendrik, and Malte Helmert. "Diverse and Additive Cartesian Abstraction Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 289–97. http://dx.doi.org/10.1609/icaps.v24i1.13639.

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We have recently shown how counterexample-guided abstraction refinement can be used to derive informative Cartesian abstraction heuristics for optimal classical planning. In this work we introduce two methods for producing diverse sets of heuristics within this framework, one based on goal facts, the other based on landmarks. In order to sum the heuristic estimates admissibly we present a novel way of finding cost partitionings for explicitly represented abstraction heuristics. We show that the resulting heuristics outperform other state-of-the-art abstraction heuristics on many benchmark domains.
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Dissertations / Theses on the topic "Goal Abstraction"

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Zadem, Mehdi. "Automatic Symbolic Goal Abstraction via Reachability Analysis in Hierarchical Reinforcement Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX141.

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L'Apprentissage par Renforcement Hiérarchique (HRL) est un paradigme qui peut être exploité pour apprendre automatiquement des stratégies pour des tâches à long terme, qui impliquent généralement plusieurs étapes à franchir avant que le problème ne soit résolu. L'idée principale de l'Apprentissage par Renforcement Hiérarchique est de diviser la tâche difficile en sous-tâches plus restreintes, qui peuvent être abordées plus facilement dans un aspect plus contraint.Un défi majeur dans le HRL est d'identifier une décomposition idéale de la tâche à long terme sous forme d'objectifs qu'un agent apprenant essaiera d'atteindre. Les environnements de haute dimension et les dynamiques complexes rendent particulièrement difficile pour l'agent de comprendre quels objectifs sont critiques pour la tâche.Cette thèse explore le concept d'apprentissage de représentations symboliques d'objectifs dans le cadre du HRL, inspiré des abstractions étudiées dans le domaine des Méthodes Formelles. Nous développons une méthode d'abstraction spatiale à partir de relations d'atteignabilité dans l'espace observable de l'environnement et fournissons des garanties sur la sous-optimalité de la politique apprise par l'agent. Nous prouvons également que l'abstraction des objectifs peut être calculée par un processus de raffinement. De plus, nous implémentons l'abstraction des objectifs tenant compte de l'atteignabilité dans un cadre d'Apprentissage par Renforcement Hiérarchique appelé GARA, créant un agent capable d'apprendre simultanément l'abstraction des objectifs et la politique. Nous montrons l'impact de l'abstraction des objectifs sur l'efficacité de l'apprentissage de l'agent, sa transférabilité et son interprétabilité sur un ensemble de tâches de navigation en basse dimension. Dans les tâches de haute dimension, les objectifs abstraits peuvent être initialement trop difficiles à atteindre avant le raffinement. Pour remédier à ce problème, nous proposons un nouvel algorithme STAR qui exploite l'abstraction spatiale tenant compte de l'atteignabilité ainsi qu'un mécanisme d'abstraction temporelle permettant plus de flexibilité sur la difficulté des objectifs choisis. Nous démontrons empiriquement que STAR surpasse l'état de l'art sur un ensemble de tâches de contrôle continu difficiles
Hierarchical Reinforcement Learning (HRL) is a paradigm that can be leveraged to automatically learn strategies for long-horizon tasks, which typically involve multiple milestones that must be achieved before the problem is solved. The main idea behind Hierarchical Reinforcement Learning is to break up the difficult task into smaller sub-tasks, that can be more easily approached under in a more constrained aspect.A core challenge in HRL is to identify an ideal decomposition of the long-horizon task in the form of goals that a learning agent will try to achieve. High-dimensional environments and complex dynamics make it particularly difficult for the agent to understand which goals are critical for the task.This thesis explores the concept of learning symbolic goal representations within HRL, inspired from abstractions studied in the field of Formal Methods. We develop a spatial abstraction method that captures reachability relations in the environment's observable space, and provide guarantees on the suboptimality of the agent's learned policy. We also prove that the goal abstraction can be computed through a process of refinement. Furthermore, we implement the reachability-aware goal abstraction with a Hierarchical Reinforcement Learning framework called GARA, creating an agent that can concurrently learn the goal abstraction and policy. We showcase the impact of the goal abstraction in the agent's learning efficiency, transferability and interpretability on a set of low-dimensional navigation tasks. In high-dimensional tasks, the abstract goals that be initially too difficult to achieve before refinement. To remedy this issue, we propose a novel algorithm STAR that leverages the reachability-aware spatial abstraction along with a temporal abstraction mechanism allowing for more flexibility on the difficulty of chosen goals. We empirically demonstrate that STAR outperforms the state of the art on a set of difficult continuous control tasks
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Denis, Nicholas. "On Hierarchical Goal Based Reinforcement Learning." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39552.

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Discrete time sequential decision processes require that an agent select an action at each time step. As humans, we plan over long time horizons and use temporal abstraction by selecting temporally extended actions such as “make lunch” or “get a masters degree”, whereby each is comprised of more granular actions. This thesis concerns itself with such hierarchical temporal abstractions in the form of macro actions and options, as they apply to goal-based Markov Decision Processes. A novel algorithm for discovering hierarchical macro actions in goal-based MDPs, as well as a novel algorithm utilizing landmark options for transfer learning in multi-task goal- based reinforcement learning settings are introduced. Theoretical properties regarding the life-long regret of an agent executing the latter algorithm are also discussed.
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Marchal, Cynthie. "Post-hoc prescience: retrospective reasoning and judgment among witnesses of interpersonal aggression." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209818.

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When judging interpersonal aggression, witnesses are usually expected to rationally consider, based on the evidence they have, what another reasonable person could (or should) have thought, known and done. However their analysis may be affected by judgment biases and personal motivations. These evaluative and retrospective biases, as well as the ascription of blame, are the main interests of this research. More specifically, we investigated the consequences of witnesses being prone to the hindsight bias, which is a common bias that gives individuals the feeling that they would have been able to predict past events, what in fact, is not the case. This process may have important effects on the victim, who “should have known” that an aggression would happen to him/her. In this dissertation, we examine the moderators of this bias and the role of the communication context in which it develops. We hypothesized that the communication context might affect the perspective that is taken on the event of interpersonal aggression and the perceived distance towards it. Also, we expected that the hindsight bias and victim blame would be decreased when reducing the psychological distance towards the event (i.e. perceived temporal distance and perceived proximity with the victim’s fate). In a same vein, we expected that the aggressor would be more derogated in this condition. The first four studies were designed to investigate the role of communication goals about the aggression. Asking participants to describe how (vs. why) the aggression happened was expected to diminish the perceived distance. The following study (study 5) examined whether reporting the event in the passive voice (vs. active voice) would have a similar effect. The four last studies investigated how the time of presenting the event (before vs. after its antecedents) would influence the perception of distance towards the events and the judgments. We expected that knowing the outcome initially might reduce the perceived distance with the events. Results of the first five studies confirmed the main hypotheses: the communication context that focused on the “how” of the event or that presented it in the passive voice reduced the perceived distance and diminished the predictability of the aggression and victim derogation. It also increases the derogation of the aggressor. In addition, the latter studies revealed that learning about the outcome right away leads to reduced derogation of the perpetrator and increased derogation of the victim, even when reducing the perceived distance with the event. Overall, this research suggests that the communication context in which the hindsight bias emerges, as well as the perceived distance with the negative event, are important factors when examining the retrospective reasoning and judgments of witnesses.

Lorsque les témoins jugent une agression interpersonnelle, il est généralement attendu d’eux qu’ils considèrent rationnellement ce qu’une personne raisonnable aurait pu penser, savoir et faire dans pareille situation, et ce en se fondant uniquement sur les preuves qui leur sont fournies. Il n’en reste pas moins que leur analyse sera toutefois tronquée par des biais de jugement et des motivations personnelles. C’est pourquoi la détermination du blâme et l’influence des déformations rétrospectives et évaluatives sont au cœur de cette recherche. Ainsi, nous investiguons plus particulièrement le biais de rétrospection, à savoir l’erreur commune qui laisse à l’individu penser qu’il est en mesure de prévoir n’importe quel événement, alors qu’en réalité, il n’en est rien. Une telle erreur peut cependant avoir de graves conséquences pour la victime dès lors que les témoins sont amenés à croire qu’elle aurait « dû » prévoir ce qui allait survenir. Dans cette thèse, nous envisageons également les modérateurs de ce biais, dont le rôle du contexte communicationnel. Nous avons, dès lors, fait l’hypothèse que le contexte communicationnel pourrait affecter l’angle sous lequel les témoins considèrent l’événement et la distance perçue par rapport à celui-ci. Ce faisant, nous pensions que le biais de rétrospection et le blâme de la victime seraient réduits lorsque le contexte diminuait la distance perçue vis-à-vis de l’événement (en l’occurrence, la distance temporelle et la proximité perçue avec le sort de la victime). De même, il était attendu que l’agresseur soit davantage blâmé dans pareille condition. Les quatre premières études s’intéressaient donc au rôle des buts poursuivis lors de la communication à propos de l’agression, afin d’envisager en quoi décrire comment (vs. pourquoi) l’agression s’était produite aidait à réduire la distance perçue. Une cinquième étude nous a ensuite permis de considérer si la voix passive (versus active) avait aussi un effet similaire. Quant aux quatre dernières études, elles avaient pour objectif d’investiguer dans quelle mesure l’ordre de présentation des informations (connaître la fin avant, vs. après les antécédents) pouvait avoir également une incidence sur la prise de distance par rapport à l’événement et aux jugements. Plus précisément, nous faisions l’hypothèse que connaître l’événement en premier lieu (avant ses antécédents) facilitait la réduction de la distance perçue. Les résultats obtenus dans les cinq premières recherches semblaient confirmer nos hypothèses :Un contexte communicationnel qui réduisait la distance psychologique perçue par rapport à l’événement pouvait non seulement diminuer le biais de rétrospection et le blâme de la victime, mais augmenter aussi le blâme de l’agresseur. Toutefois, les dernières recherches ont semblé démontrer, a contrario, que connaître l’agression en premier lieu pouvait réduire le blâme de l’agresseur et augmenter celui de la victime, alors même que la distance perçue avec les événements était réduite. In fine, ce travail suggère donc que le contexte communicationnel, dans lequel le biais émerge, et la prise de distance face à l’événement négatif sont autant de pistes qu’il faudrait creuser à l’avenir pour mieux comprendre le raisonnement et les jugements rétrospectifs des témoins.
Doctorat en Sciences Psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished

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Jardim, David Walter Figueira. "Hierarchical reinforcement learning: learning sub-goals and state-abstraction." Master's thesis, 2010. http://hdl.handle.net/10071/2866.

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Os seres humanos possuem a incrível capacidade de criar e utilizar abstracções. Com essas abstracções somos capazes de resolver tarefas extremamente complexas que requerem muita antevisão e planeamento. A pesquisa efectuada em Hierarchical Reinforcement Learning demonstrou a utilidade das abstracções, mas também introduziu um novo problema. Como encontrar uma maneira de descobrir de forma autónoma abstracções úteis e criá-las enquanto aprende? Neste trabalho, apresentamos um novo método que permite a um agente descobrir e criar abstracções temporais de forma autónoma. Essas abstracções são baseadas na framework das Options. O nosso método é baseado no conceito de que para alcançar o objectivo, o agente deve passar por determinados estados. Ao longo do tempo estes estados vão começar a diferenciar-se dos restantes, e serão identificados como sub-objectivos úteis. Poderão ser utilizados pelo agente para criar novas abstracções temporais, cujo objectivo é ajudar a atingir esses objectivos secundários. Para detectar subobjectivos, o nosso método cria intersecções entre os vários caminhos que levam ao objectivo principal. Para que uma tarefa seja resolvida com sucesso, o agente deve passar por certas regiões do espaço de estados, estas regiões correspondem à nossa definição de sub-objectivos. A nossa investigação focou-se no problema da navegação em salas, e também no problema do táxi. Concluímos que um agente pode aprender mais rapidamente em problemas mais complexos, ao automaticamente descobrir sub-objectivos e criar abstracções sem precisar de um programador para fornecer informações adicionais e de criar as abstracções manualmente.
Human beings have the incredible capability of creating and using abstractions. With these abstractions we are able to solve extremely complex tasks that require a lot of foresight and planning. Research in Hierarchical Reinforcement Learning has demonstrated the utility of abstractions, but, it also has introduced a new problem. How can we find a way to autonomously discover and create useful abstractions while learning? In this dissertation we present a new method that allows an agent to discover and create temporal abstractions autonomously based in the options framework. Our method is based on the concept that to reach the goal, the agent must pass through certain states. Throughout time these states will begin to differentiate from others, and will be detected as useful subgoals and be used by the agent to create new temporal abstractions, whose objective is to help achieve these subgoals. To detect useful subgoals, our method creates intersections between several paths leading to a goal. In order for a task to be solved successfully the agent must pass through certain regions of the state space, these regions will correspond to our definition of subgoals. Our research focused on domains largely used in the study of the utility of temporal abstractions, which is the room-to-room navigation problem, and also the taxi problem. We determined that, in the problems tested, an agent can learn more rapidly in more complex problems by automatically discovering subgoals and creating abstractions without needing a programmer to provide additional information and handcraft the abstractions.
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Books on the topic "Goal Abstraction"

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Andrew, Nell. Moving Modernism. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190057275.001.0001.

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This book reenacts the simultaneous eruption of three spectacular revolutions—the development of pictorial abstraction, the first modern dance, and the birth of cinema—which together changed the artistic landscape of early twentieth-century Europe and the future of modern art. Rather than seeking dancing pictures or pictures of dancing, however, this study follows the chronology of the historical avant-garde to show how dance and pictures were engaged in a kindred exploration of the limits of art and perception that required the process of abstraction. Recovering the performances, methods, and circles of aesthetic influence of avant-garde dance pioneers and experimental filmmakers from the turn of the century to the interwar period, this book challenges modernism’s medium-specific frameworks by demonstrating the significant role played by the arts of motion in the historical avant-garde’s development of abstraction: from the turn-of-the-century dancer Loïe Fuller, who awakened in symbolist artists the possibility of prolonged vision; to cubo-futurist and neosymbolist artists who reached pure abstraction in tandem with the radical dance theory of Valentine de Saint-Point; to Sophie Taeuber’s hybrid Dadaism between art and dance; to Akarova, a prolific choreographer whose dancing Belgian constructivist pioneers called “music architecture”; and finally to the dancing images of early cinematic abstraction from the Lumière brothers to Germaine Dulac. Each chapter reveals the emergence of abstractionas an apparatus of creation, perception, and reception deployed across artistic media toward shared modernist goals. The author argues that abstraction can be worked like a muscle, a medium through which habits of reception and perception are broken and art’s viewers are engaged by the kinesthetic sensation to move and be moved.
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Hellman, Geoffrey, and Stewart Shapiro. The Matter of Points. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198712749.003.0007.

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This chapter turns to metaphysical matters. Some analytic metaphysicians have occupied themselves with the nature (or the possible nature) of space and time (or space–time), and with the relationship between physical objects and the regions of space or space–time they occupy. Some of the issues concern the boundaries of objects and the notion of contact. The first goal is to give a somewhat biased overview of a portion of this literature, arguing that many of the issues are much easier to negotiate if we assume a regions-based space or space–time. The chapter then turns to some apparent limitations of the semi-Aristotelian accounts of space or space–time. For example, the natural analogue of Lebesgue measure is not countably additive (although it is finitely additive), and there seems to be no straightforward way to account for continuous variation in our frameworks other than by just introducing “points” via “extensive abstraction”. Finally, the question is broached of adjudicating whether space or space–time really is punctiform. The tight connection between our regions-based, gunky theories and the more standard Dedekind–Cantor punctiform theories indicates that space or space–time can be described, completely and adequately either way.
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Olejnik, Iwona, ed. Qualitative and quantitative methods in sustainable development. Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, 2021. http://dx.doi.org/10.18559/978-83-8211-072-2.

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Systematic research and comprehensive analyses allow to monitor the implementation of the sustainable development goals. Obviously, when you are interested in the selected issue of sustainable development, it is worth using data from the secondary sources in the first place. This e-book presents a few selected methods that will allow you to answer the questions: how to gather data and how to analyse them? Among the data collection methods presented in this book, we have chosen both: qualitative, in particular focus group interview, and quantitative—based on a questionnaire. In terms of data analysis methods, we present three methods: factor analysis, structural equation modelling and data envelopment analysis. The examples presented in this book relate to sustainable development, for example: sustainable consumption, ecological culture, better nutrition, agricultural development and many more. The book consists of five chapters. Chapter 1 “Qualitative methods” presents the issues concerning the methodology of qualitative research, designing a focus group interview, conducting a focus group interview and analysis of qualitative data using the CAQDAS programs. The main goal of Chapter 2 titled “Quantitative methods” is to exhibit the basics of survey research that can be used in analyses of sustainable development. In particular, this part presents the measurement levels, questionnaire design, population and sample, and the ways of presenting the results of quantitative research. Chapter 3 “Factor analysis in sustainable development research” describes the basic theoretical aspects of factor analysis. The second part of this chapter presents an example of the use of this method in research on sustainable consumption. The last part of this chapter presents case study of the use of factor analysis in research on managers’ ethics in retail industry. Chapter 4 titled “Structural equation modelling in sustainable development research” is dedicated to the structural equation modeling methods applied to solve sustainable development research problems. A structural equation model is an abstraction of reality, and the researcher's job is to build a model that approximates that reality as closely as possible. And the aim of Chapter 5 “Data envelopment analysis methods in sustainable agricultural development research” is to determine the relative technical efficiency of representative agricultural farms from the individual European Union countries.
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Glennan, Stuart. Models, Mechanisms, and How Explanations. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198779711.003.0003.

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This chapter argues that models are the central means by which scientists represent phenomena and the mechanisms responsible for them. Extending work by Giere and Weisberg, it offers a general account of models, which emphasizes that models are constructed for particular purposes and are related to their targets by similarity relations. Different goals will lead to different models, so that a given target has many models. It then identifies the features that distinguish mechanistic models from other models, and shows how these models are used in explanation. Abstraction and idealization are essential to the modeling process, and the chapter shows how these features allow scientists to provide general representations of heterogeneous mechanisms.
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Garland, Baalla. Congratulations on Your New Job Abstractor: Abstractor Gift - Blank Lined Notebook Job Congratulations Gifts. This Journal Is a Perfect for Taking Notes, Ideas, Writing Goals and Plans, or Writing Diary. Independently Published, 2021.

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Williams, Scott M. John Duns Scotus. Edited by William J. Abraham and Frederick D. Aquino. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199662241.013.12.

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This chapter argues that John Duns Scotus has several goals in the epistemology of theology: logical consistency, certainty, truth, and right praxis. The first section covers the natural knowledge of God, in which Scotus defends the claim that there are some non-complex univocal concepts, that they can be the building blocks of complex analogical concepts, and that univocal and analogical concepts are applicable to God and to creatures. A genealogy is given of three exegetical mistakes regarding univocity made by some twentieth-century thinkers. The second section covers five ways that one can have supernatural knowledge of God: intuitive cognition, abstractive cognition with the possibility of doubt, abstractive cognition without the opportunity for doubt, biblical exegesis, and faith. The next section discusses the scientific character of theology, and how such knowledge is literally a part of the praxis of loving God. The conclusion discusses how theology can resolve dilemmas in a naturalistic epistemology.
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Haskell, Ellen. A Composite Countenance. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190636647.003.0007.

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The thirteenth-century Spanish Jewish mystical classic Sefer ha-Zohar is known for its elaborate divine imagery. This chapter explains how the Zohar invests the traditional anthropomorphic metaphor of the divine countenance with new meaning in order to define both divine and human faces as sites of spiritual revelation and transformation. The Zoharic authors’ goals are twofold. First, the mystics’ own human faces are divinized, becoming vehicles of mutual revelation accessed through spiritual fellowship. Second, the divine face is defined as an abstraction beyond human understanding, since human features are but one fragment of a transcendent whole that inspires contemplation through unusual image juxtapositions. This dual usage mirrors the Zohar’s broader mystical theology, which understands God as both revealed to and concealed from human beings. Further, reworking an ancient divine metaphor from within by manipulating its metonymic associations allows the mystics to transform their religious culture without overtly defying traditional scriptural authority.
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Butz, Martin V., and Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.

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For more than 2000 years Greek philosophers have thought about the puzzling introspectively assessed dichotomy between our physical bodies and our seemingly non-physical minds. How is it that we can think highly abstract thoughts, seemingly fully detached from actual, physical reality? Despite the obvious interactions between mind and body (we get tired, we are hungry, we stay up late despite being tired, etc.), until today it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. Despite a big movement towards embodied cognitive science over the last 20 years or so, introductory books with a functional and computational perspective on how human thought and language capabilities may actually have come about – and are coming about over and over again – are missing. This book fills that gap. Starting with a historical background on traditional cognitive science and resulting fundamental challenges that have not been resolved, embodied cognitive science is introduced and its implications for how human minds have come and continue to come into being are detailed. In particular, the book shows that evolution has produced biological bodies that provide “morphologically intelligent” structures, which foster the development of suitable behavioral and cognitive capabilities. While these capabilities can be modified and optimized given positive and negative reward as feedback, to reach abstract cognitive capabilities, evolution has furthermore produced particular anticipatory control-oriented mechanisms, which cause the development of particular types of predictive encodings, modularizations, and abstractions. Coupled with an embodied motivational system, versatile, goal-directed, self-motivated behavior, learning becomes possible. These lines of thought are introduced and detailed from interdisciplinary, evolutionary, ontogenetic, reinforcement learning, and anticipatory predictive encoding perspectives in the first part of the book. A short excursus then provides an introduction to neuroscience, including general knowledge about brain anatomy, and basic neural and brain functionality, as well as the main research methodologies. With reference to this knowledge, the subsequent chapters then focus on how the human brain manages to develop abstract thought and language. Sensory systems, motor systems, and their predictive, control-oriented interactions are detailed from a functional and computational perspective. Bayesian information processing is introduced along these lines as are generative models. Moreover, it is shown how particular modularizations can develop. When control and attention come into play, these structures develop also dependent on the available motor capabilities. Vice versa, the development of more versatile motor capabilities depends on structural development. Event-oriented abstractions enable conceptualizations and behavioral compositions, paving the path towards abstract thought and language. Also evolutionary drives towards social interactions play a crucial role. Based on the developing sensorimotor- and socially-grounded structures, the human mind becomes language ready. The development of language in each human child then further facilitates the self-motivated generation of abstract, compositional, highly flexible thought about the present, past, and future, as well as about others. In conclusion, the book gives an overview over how the human mind comes into being – sketching out a developmental pathway towards the mastery of abstract and reflective thought, while detailing the critical body and neural functionalities, and computational mechanisms, which enable this development.
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Book chapters on the topic "Goal Abstraction"

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Okubo, Yoshiaki, and Makoto Haraguchi. "Constructing predicate mappings for Goal-Dependent Abstraction." In Lecture Notes in Computer Science, 516–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58520-6_87.

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Barth, Max, Daniel Dietsch, Matthias Heizmann, and Marie-Christine Jakobs. "Ultimate TestGen: Test-Case Generation with Automata-based Software Model Checking (Competition Contribution)." In Fundamental Approaches to Software Engineering, 326–30. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57259-3_20.

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AbstractWe introduce Ultimate TestGen, a novel tool for automatic test-case generation. Like many other test-case generators, Ultimate TestGen builds on verification technology, i.e., it checks the (un)reachability of test goals and generates test cases from counterexamples. In contrast to existing tools, it applies trace abstraction, an automata-theoretic approach to software model checking, which is implemented in the successful verifier Ultimate Automizer. To avoid that the same test goal is reached again, Ultimate TestGen extends the automata-theoretic model checking approach with error automata.
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Yang, Pengfei, Renjue Li, Jianlin Li, Cheng-Chao Huang, Jingyi Wang, Jun Sun, Bai Xue, and Lijun Zhang. "Improving Neural Network Verification through Spurious Region Guided Refinement." In Tools and Algorithms for the Construction and Analysis of Systems, 389–408. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72016-2_21.

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AbstractWe propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze the network. If the robustness property cannot be verified, the result is inconclusive. Due to the over-approximation, the computed region in the abstraction may be spurious in the sense that it does not contain any true counterexample. Our goal is to identify such spurious regions and use them to guide the abstraction refinement. The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons. This is achieved by linear programming techniques. With the new bounds, we iteratively apply DeepPoly, aiming to eliminate spurious regions. We have implemented our approach in a prototypical tool DeepSRGR. Experimental results show that a large amount of regions can be identified as spurious, and as a result, the precision of DeepPoly can be significantly improved. As a side contribution, we show that our approach can be applied to verify quantitative robustness properties.
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Cirisci, Berk, Constantin Enea, and Suha Orhun Mutluergil. "Quorum Tree Abstractions of Consensus Protocols." In Programming Languages and Systems, 337–62. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30044-8_13.

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AbstractDistributed algorithms solving agreement problems like consensus or state machine replication are essential components of modern fault-tolerant distributed services. They are also notoriously hard to understand and reason about. Their complexity stems from the different assumptions on the environment they operate with, i.e., process or network link failures, Byzantine failures etc. In this paper, we propose a novel abstract representation of the dynamics of such protocols which focuses on quorums of responses (votes) to a request (proposal) that form during a run of the protocol. We show that focusing on such quorums, a run of a protocol can be viewed as working over a tree structure where different branches represent different possible outcomes of the protocol, the goal being to stabilize on the choice of a fixed branch. This abstraction resembles the description of recent protocols used in Blockchain infrastructures, e.g., the protocol supporting Bitcoin or Hotstuff. We show that this abstraction supports reasoning about the safety of various algorithms, e.g., Paxos, PBFT, Raft, and HotStuff, in a uniform way. In general, it provides a novel induction based argument for proving that such protocols are safe.
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Bertrand, Yannis, Bram Van den Abbeele, Silvestro Veneruso, Francesco Leotta, Massimo Mecella, and Estefanía Serral. "A Survey on the Application of Process Mining to Smart Spaces Data." In Lecture Notes in Business Information Processing, 57–70. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_5.

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AbstractDuring the last years, a number of studies have experimented with applying process mining (PM) techniques to smart spaces data. The general goal has been to automatically model human routines as if they were business processes. However, applying process-oriented techniques to smart spaces data comes with its own set of challenges. This paper surveys existing approaches that apply PM to smart spaces and analyses how they deal with the following challenges identified in the literature: choosing a modelling formalism for human behaviour; bridging the abstraction gap between sensor and event logs; and segmenting logs in traces. The added value of this article lies in providing the research community with a common ground for some important challenges that exist in this field and their respective solutions, and to assist further research efforts by outlining opportunities for future work.
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Müller, Rainer, and Martin Karkowski. "Generic Modeling Technique for Flexible and Highly Available Assembly Systems." In Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021, 3–14. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-74032-0_1.

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AbstractTo face the major challenges posed by the increasing product variants with shortening life cycles and fluctuating market conditions, modular and adaptable assembly systems are used. Their strong dependency on software creates a new void in the planning and implementation processes of these assembly systems. Usually, the programmer fills this void based on his knowledge, which leads to frequent and large adaptations of the code base. This is rather counter-productive. To address this challenge, we developed a generic user-friendly graphical API (Application Programming Interface) for a process owner in our previous work (Müller et al, Proc CIRP 81:730–735, 2019, [1]). This API can describe any assembly system and the associated task and is used to get the boilerplate code needed to execute the process on a programmable logic controller (PLC)—the standard hardware used in the industry. In this paper, the virtual description of an assembly system used by the API is extended to include a goal-oriented task description by defining the process and the structure. We believe that this extension provides the proper abstraction needed by the process owner. In addition, this extension significantly reduces the modeling effort.
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Maranhão Junior, João José, Filipe F. Correia, and Eduardo Martins Guerra. "Can ChatGPT Suggest Patterns? An Exploratory Study About Answers Given by AI-Assisted Tools to Design Problems." In Lecture Notes in Business Information Processing, 130–38. Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-72781-8_14.

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AbstractGeneral-purpose AI-assisted tools, such as ChatGPT, have recently gained much attention from the media and the general public. That raised questions about in which tasks we can apply such a tool. A good code design is essential for agile software development to keep it ready for change. In this context, identifying which design pattern can be appropriate for a given scenario can be considered an advanced skill that requires a high degree of abstraction and a good knowledge of object orientation. This paper aims to perform an exploratory study investigating the effectiveness of an AI-assisted tool in assisting developers in choosing a design pattern to solve design scenarios. To reach this goal, we gathered 56 existing questions used by teachers and public tenders that provide a concrete context and ask which design pattern would be suitable. We submitted these questions to ChatGPT and analyzed the answers. We found that 93% of the questions were answered correctly with a good level of detail, demonstrating the potential of such a tool as a valuable resource to help developers to apply design patterns and make design decisions.
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Dorst, Leo. "Bottom-up derivation of the qualitatively different behaviors of a car across varying spatio-temporal scales: A study in abstraction of goal-directed motion." In Algebraic Frames for the Perception-Action Cycle, 344–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0017877.

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Casas, Robert D. Thompson. "Applying DATEMATS Methods and Tools to Nanomaterials: A Design Challenge by the Company Antolin." In Materialising the Future, 83–101. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25207-5_5.

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AbstractA current area of R&D focuses on developing optimal workshop methodologies which are based on didact-creative programmes specifically tailored to stimulate creative insight within participants, through the delivery of perfected techniques in both knowledge transfer and creative development sessions. The overarching goal of these methods is to develop and deliver new and useful projected applications of material properties and technologies which, when combined, can progress into new and previously unforeseen advancements in innovation across diverse fields of applicability. Here, contending technologies, whichever they may be, are presented to a select public of participants, followed by various collaborative creativity techniques whereby the assimilated information is collected, categorized and then reassimilated into new forms of innovative ordering, structuring and integrated storytelling. Although a number of creativity and technology building workshops exist, this particular study relates to the methods for developing new art applications from distinct physio-chemical traits found among a diverse collection of nanotech materials, including and in particular, carbon-based ones. An object of this paper is to disclose the specific didactic and creativity techniques used in a workshop setting which was performed in collaboration with the Antolin group who is a manufacturer and provider of helical carbon nanofibers. A further objective of this paper is to derive conclusive evaluations and insights regarding the successes and failures encountered during the knowledge transfer phases and their conversion into creative insights and market potential. Various creativity fostering strategies are presented as they were adopted through third-party mediated practices in similarity-finding, inductive and deductive reasoning, exercises in free-association/abstraction and visual Imagineering of scientifically supported product outcomes.
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Suazo Laguna, Harold Agusto. "An Autobiographical Perspective on Community-Based Participatory Research, an Approach for More Inclusive Research in Nicaragua." In Sustainable Development Goals Series, 243–54. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-53793-6_17.

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AbstractIn this chapter, I share my perspective on the role that we, researchers, play when developing a community-centered research project. Through my autobiographical narrative, I share experiences and lessons learned over two decades as a social scientist, facilitating and implementing diverse methodologies that have been oriented towards the development of the organizational skills of communities and their role as protagonists in identifying solutions for their own health problems.
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Conference papers on the topic "Goal Abstraction"

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Ilcheva, Irena, Vesela Zaharieva, Anna Yordanova, and Snejanka Balabanova. "CATCHMENT ABSTRACTION MANAGEMENT STRATEGY AND ECOLOGICAL FLOW DETERMINATION IN CASE OF NATURA 2000 AREAS." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024, 49–58. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/3.1/s12.06.

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A classical approach to the assessment of the available water resources for different users, including environmental, in Europe is the Catchment Abstraction Management Strategy (CAMS). An assessment has to be made of the extent, to which natural runoff regime could be changed without affecting the sustainable functioning of the environment. The aim of the Restoring Sustainable Abstraction (RSA) is to identify over-abstraction in rivers and wetland sites and make the abstraction sustainable again. In this regard, the report presents an innovative approach for the integrated assessment and management of the water and natural resources (water ecosystems, wetlands, forest, etc.) in cases of Natura 2000 areas (on the example of Vitosha Nature Park, Sanitary Protection Zones of the Struma River Basin, etc.). In the methodology, the ecosystems are also part of the measures. Some measures (such as to restore water regime of the peatlands) are non-traditional measures for RSA and supply-side drought measure. This is a step towards improving the CAMS approach. The main stages of the improved methodology are: Modeling and assessment of climate factors and trends; Water balance modeling; Assessment of water consumption and the environmental needs; Ecological flow assessment and RSA; Simulation modeling and CAMS; Monitoring and adaptive management. A methodology and a mathematical model for determination the ecological flow in Bulgaria which is a necessary condition for achieving a good ecological status are proposed. As an object of research macrozoobentos ecosystem are considered. Emphasis is given to the places of water abstraction, water transfer, rivers at risk of not achieving good ecological potential (hot spots). Measures with synergistic effects are recommended � to protect ecosystems, for flood risk prevention, etc. The results and applications in practice are related to the Vitosha Nature Park Management Plan, Program of Measures, for the goals of WFD and Natura 2000 and supports the MoEW and Basin Directorates in determining the ecological flow and implementation the Eflow concept in Bulgaria
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Muhammad, Umar Riaz, Yongxin Yang, Timothy Hospedales, Tao Xiang, and Yi-Zhe Song. "Goal-Driven Sequential Data Abstraction." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00016.

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Cui, Zhenhe, Yongmei Liu, and Kailun Luo. "A Uniform Abstraction Framework for Generalized Planning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/253.

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Generalized planning aims at finding a general solution for a set of similar planning problems. Abstractions are widely used to solve such problems. However, the connections among these abstraction works remain vague. Thus, to facilitate a deep understanding and further exploration of abstraction approaches for generalized planning, it is important to develop a uniform abstraction framework for generalized planning. Recently, Banihashemi et al. proposed an agent abstraction framework based on the situation calculus. However, expressiveness of such an abstraction framework is limited. In this paper, by extending their abstraction framework, we propose a uniform abstraction framework for generalized planning. We formalize a generalized planning problem as a triple of a basic action theory, a trajectory constraint, and a goal. Then we define the concepts of sound abstractions of a generalized planning problem. We show that solutions to a generalized planning problem are nicely related to those of its sound abstractions. We also define and analyze the dual notion of complete abstractions. Finally, we review some important abstraction works for generalized planning and show that they can be formalized in our framework.
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Delgado, H. Mayela, Francisca Losavio, and Alfredo Matteo. "Goal oriented techniques and methods: Goal refinement and levels of abstraction." In 2013 Latin American Computing Conference (CLEI). IEEE, 2013. http://dx.doi.org/10.1109/clei.2013.6670631.

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Surynek, Pavel. "Counterexample Guided Abstraction Refinement with Non-Refined Abstractions for Multi-Goal Multi-Robot Path Planning." In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023. http://dx.doi.org/10.1109/iros55552.2023.10341952.

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Kim, Donghoon, Minjong Yoo, and Honguk Woo. "Offline Policy Learning via Skill-step Abstraction for Long-horizon Goal-Conditioned Tasks." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/473.

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Goal-conditioned (GC) policy learning often faces a challenge arising from the sparsity of rewards, when confronting long-horizon goals. To address the challenge, we explore skill-based GC policy learning in offline settings, where skills are acquired from existing data and long-horizon goals are decomposed into sequences of near-term goals that align with these skills. Specifically, we present an `offline GC policy learning via skill-step abstraction' framework (GLvSA) tailored for tackling long-horizon GC tasks affected by goal distribution shifts. In the framework, a GC policy is progressively learned offline in conjunction with the incremental modeling of skill-step abstractions on the data. We also devise a GC policy hierarchy that not only accelerates GC policy learning within the framework but also allows for parameter-efficient fine-tuning of the policy. Through experiments with the maze and Franka kitchen environments, we demonstrate the superiority and efficiency of our GLvSA framework in adapting GC policies to a wide range of long-horizon goals. The framework achieves competitive zero-shot and few-shot adaptation performance, outperforming existing GC policy learning and skill-based methods.
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Zadem, Mehdi, Sergio Mover, and Sao Mai Nguyen. "Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis." In 2023 IEEE International Conference on Development and Learning (ICDL). IEEE, 2023. http://dx.doi.org/10.1109/icdl55364.2023.10364473.

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Okubo, Yoshiaki, and Makoto Haraguchi. "Attacking legal argument by examining stability of case citation with goal-dependent abstraction." In the sixth international conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/261618.261652.

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Banihashemi, Bita, Giuseppe De Giacomo, and Yves Lesperance. "Abstraction of Nondeterministic Situation Calculus Action Theories." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/347.

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We develop a general framework for abstracting the behavior of an agent that operates in a nondeterministic domain, i.e., where the agent does not control the outcome of the nondeterministic actions, based on the nondeterministic situation calculus and the ConGolog programming language. We assume that we have both an abstract and a concrete nondeterministic basic action theory, and a refinement mapping which specifies how abstract actions, decomposed into agent actions and environment reactions, are implemented by concrete ConGolog programs. This new setting supports strategic reasoning and strategy synthesis, by allowing us to quantify separately on agent actions and environment reactions. We show that if the agent has a (strong FOND) plan/strategy to achieve a goal/complete a task at the abstract level, and it can always execute the nondeterministic abstract actions to completion at the concrete level, then there exist a refinement of it that is a (strong FOND) plan/strategy to achieve the refinement of the goal/task at the concrete level.
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Banihashemi, Bita, Giuseppe De Giacomo, and Yves Lespérance. "Abstraction of Agents Executing Online and their Abilities in the Situation Calculus." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/235.

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We develop a general framework for abstracting online behavior of an agent that may acquire new knowledge during execution (e.g., by sensing), in the situation calculus and ConGolog. We assume that we have both a high-level action theory and a low-level one that represent the agent's behavior at different levels of detail. In this setting, we define ability to perform a task/achieve a goal, and then show that under some reasonable assumptions, if the agent has a strategy by which she is able to achieve a goal at the high level, then we can refine it into a low-level strategy to do so.
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Reports on the topic "Goal Abstraction"

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Pham, H., T. Budge, and R. Nell. Predictive Flow Simulation with the P2R Model for the 200-IA-1 Preliminary Remediation Goal Saturated Zone Abstraction. Office of Scientific and Technical Information (OSTI), January 2022. http://dx.doi.org/10.2172/1842314.

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Sinfield, Joseph, and Romika Kotian. Framing Complex Challenges. Purdue University, August 2023. http://dx.doi.org/10.5703/1288284317649.

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Innovating to achieve impact at scale in ill-structured environments is a complex challenge, with numerous socio-techno-economic dimensions. Whether tailoring a product or service offering to meet the needs of a global customer base, forecasting the socio-economic implications of a new technology, or working to address a societal challenge in a low-to-middle income country, problems that encompass social, technical and/or economic uncertainty, significant variations in involved stakeholder characteristics, and even subtle differences across the focal environment, inevitably benefit from a holistic analysis that spans multiple levels of abstraction, encompasses a plurality of perspectives, and can be tailored to include contextual nuances. In addition, and perhaps most importantly, these types of problems require an accurate definition of the problem to be solved. To foster effective progress in this class of challenges, we herein introduce a novel problem analysis method, termed Comprehensive Success Factor Analysis (CSFA), that provides a rigorous and structured approach to attain holistic problem frames across an array of challenge types to inform decision making, innovation, and capacity building with the goal of achieving impact at scale.
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Poloboc, Alina. Fancy Pink Goat. Intellectual Archive, December 2023. http://dx.doi.org/10.32370/iaj.2998.

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"Fancy Pink Goat" is a contemporary art piece from the Fancy Collection, created in Spain in 2022. It is a vividly colorful painting dominated by pink and blue, which are the signature colors of the artist`s style. The painting features a fancy goat walking through the jungle with its elegant collar and abstract, long legs. Surrounding the Fancy Pink Goat are a variety of other unusual creatures inhabiting the jungle and keeping the goat company. The artist`s signature red high-heeled shoes are also present, adding a touch of sophistication and style to the painting. This artwork is an impressive example of the artist`s unique style, which blends elements of surrealism and abstraction to create a sense of fantasy and wonder. The overall effect is an intriguing and vibrant work of art that captures the viewer`s imagination. With its expert technique and distinctive style, "Fancy Pink Goat" is truly a gem in the Fancy Collection.
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