Gotowe bibliografie tematyczne / Goal-Oriented AI

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Części książek
  4. Streszczenia konferencji

Gotowa bibliografia na temat „Goal-Oriented AI”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Goal-Oriented AI"

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Perera, Ravindu, Claire Gendron, Cecilia Delgado, et al. "AI Assistants in the Workplace: Goal-Oriented Recommendations Using LLM." Avances en Interacción Humano-Computadora 9, no. 1 (2024): 16–20. https://doi.org/10.47756/aihc.y9i1.140.

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Self-quantifying technology enables users to evaluate their performance and define strategies for improvement. Workplace technology assists users in identifying activities and patterns that facilitate task completion. Software that measures workplace signals requires access to information from the user interface and peripherals. Current software solutions that track computer activity lack goal orientation and do not share raw data that could aid users and researchers in analyzing behavioral patterns at work. This paper presents Wellbot, an intelligent AI assistant capable of tracking workers'
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Chromiak, Michał. "Heterogeneous Indexing Register for Object Database." Annales Universitatis Mariae Curie-Sklodowska, sectio AI – Informatica 16, no. 2 (2017): 7. http://dx.doi.org/10.17951/ai.2016.16.2.7.

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<p>Even though the object oriented persistent stores has not gained large commercial adaptation rate, it still is an interesting research field in many aspects including the data integration. Persistent data integration is a very challenging goal in modern computer systems. This paper presents a proposal for application of effective indexing integration scheme for distributed and heterogeneous data environment using an object database as the central store.</p>
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Zhang, Lu. "Digital Circuit Course Development Based On AI." International Journal of Education and Social Development 3, no. 1 (2025): 100–102. https://doi.org/10.54097/e7ybbg51.

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Artificial Intelligence (AI) is presenting unprecedented opportunities and challenges to higher education, making AI-driven curriculum reform imperative. By constructing a knowledge graph for the digital circuit course, this approach enables students to engage in goal-oriented learning processes through understanding the framework of key concepts, while utilizing assessment questions linked to specific knowledge points for self-evaluation. This AI-powered transformation can significantly enhance students' mastery of the course content.
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Kabashkin, Igor, Olga Zervina, and Boriss Misnevs. "AI Narrative Modeling: How Machines’ Intelligence Reproduces Archetypal Storytelling." Information 16, no. 4 (2025): 319. https://doi.org/10.3390/info16040319.

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This study examines how large language models reproduce Jungian archetypal patterns in storytelling. Results indicate that AI excels at replicating structured, goal-oriented archetypes (Hero, Wise Old Man), but it struggles with psychologically complex and ambiguous narratives (Shadow, Trickster). Expert evaluations confirmed these patterns, rating AI higher on narrative coherence and thematic alignment than on emotional depth and creative originality.
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Jadhav, Aishwarya, Jeffery Cao, Abhishree Shetty, et al. "AI Guide Dog: Egocentric Path Prediction on Smartphone." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 220–27. https://doi.org/10.1609/aaaiss.v5i1.35591.

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This paper presents AI Guide Dog (AIGD), a lightweight egocentric (first-person) navigation system for visually impaired users, designed for real-time deployment on smartphones. AIGD employs a vision-only multi-label classification approach to predict directional commands, ensuring safe navigation across diverse environments. We introduce a novel technique for goal-based outdoor navigation by integrating GPS signals and high-level directions, while also handling uncertain multi-path predictions for destination-free indoor navigation. As the first navigation assistance system to handle both goa
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Shao, Tiejun, and Jianshe Zhou. "Brief Overview of Intelligent Education." Journal of Contemporary Educational Research 5, no. 8 (2021): 187–92. http://dx.doi.org/10.26689/jcer.v5i8.2460.

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Intelligent Education uses AI technology as a means in the education ecology to promote the automation and intelligence of education and teaching. It reshapes the education ecology, adding AI things to the traditional education ecology that dominated by teachers and students. Although IE technology is widely used, there is little discussion about a comprehensive overview of IE. The goal and connotation of IE is discussed. Meanwhile, the emotional, ethical, AI technology as well as supervision and management perspectives in IE are discussed too. The core goal of IE is putted forward that is hum
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Lin, Jessy, Nicholas Tomlin, Jacob Andreas, and Jason Eisner. "Decision-Oriented Dialogue for Human-AI Collaboration." Transactions of the Association for Computational Linguistics 12 (2024): 892–911. http://dx.doi.org/10.1162/tacl_a_00679.

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Abstract We describe a class of tasks called decision-oriented dialogues, in which AI assistants such as large language models (LMs) must collaborate with one or more humans via natural language to help them make complex decisions. We formalize three domains in which users face everyday decisions: (1) choosing an assignment of reviewers to conference papers, (2) planning a multi-step itinerary in a city, and (3) negotiating travel plans for a group of friends. In each of these settings, AI assistants and users have disparate abilities that they must combine to arrive at the best decision: Assi
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Palmer, Derek, Yifan Zhu, Kenneth Lai, et al. "Speech Is Not Enough: Interpreting Nonverbal Indicators of Common Knowledge and Engagement." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29676–78. https://doi.org/10.1609/aaai.v39i28.35364.

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Our goal is to develop an AI Partner that can provide support for group problem solving and social dynamics. In multi-party working group environments, multimodal analytics is crucial for identifying non-verbal interactions of group members. In conjunction with their verbal participation, this creates an holistic understanding of collaboration and engagement that provides necessary context for the AI Partner. In this demo, we illustrate our present capabilities at detecting and tracking nonverbal behavior in student task-oriented interactions in the classroom, and the implications for tracking
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Hong, Yoosung, Tianhao Yan, and Jinseok Seo. "GOBT: A Synergistic Approach to Game AI Using Goal-Oriented and Utility-Based Planning in Behavior Trees." Journal of Multimedia Information System 10, no. 4 (2023): 321–32. http://dx.doi.org/10.33851/jmis.2023.10.4.321.

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Boeda, Gautier. "Multi-Agent Cooperation in Games with Goal Oriented Action Planner: Use Case in WONDER Prototype Project." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 17, no. 1 (2021): 204–7. http://dx.doi.org/10.1609/aiide.v17i1.18909.

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Multi-agent cooperation systems often rely on an external commander-like entity that will plan for all the agents it manages. When it comes to games development, this entity can be hidden from the player, which makes the player believe the characters are actually taking their own decisions. However, it has limitations. We believe that to achieve truly believable character-interactions between non-playable characters, real(non-scripted) communication between the agents is the key. We will introduce our multi-agent cooperation system where each AI-agent thinks for itself and communicates with th
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Rozprawy doktorskie na temat "Goal-Oriented AI"

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Hall, Tobias, and Matteus Magnusson. "Adaptive Goal Oriented Action Planning for RTS Games." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4362.

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This thesis describes the architecture of an adaptive goal-oriented AI system that can be used for Real-Time Strategy games. The system is at the end tested against a single opponent on three di erent maps with di erent sizes to test the ability of the AI opposed to the 'standard' Finite State Machines and the likes in Real-Time Strategy games. The system consists of a task handler agent that manages all the active and halted tasks. A task is either low-level; used for ordering units, or high-level that can form advanced strategies. The General forms plans that are most advantageous
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Olofsson, Malmberg William. "Utvärdering av sökriktningar i Goal-Oriented Action Planning." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15608.

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Goal-Oriented Action Planning, även kallat GOAP, är ett system för att styra beteende av artificiell intelligens. Systemet använder en sökalgoritm för att besluta vilket beteende som ska köras baserat på ett mål och ett antal åtgärder. Studien målsatte att undersöka vilken sökriktning som var mest lämpad för givet scenario. Undersökningen utfördes med hjälp av en simpel spelprototyp baserat på rollspel med stridsmekaniker. Totalt tre tester utfördes med olika scenarion och alla resulterade i att regressiv sökning var snabbare än progressiv sökning. Resultat för det mest krävande målet visade a
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Magnusson, Matteus, and Tobias Hall. "Adaptive goal oriented action planning for RTS games." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4361.

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This thesis describes the architecture of an adaptive goal-oriented AI system that can be used for Real-Time Strategy games. The system is at the end tested against a single opponent on three different maps with different sizes to test the ability of the AI opposed to the 'standard' Finite State Machines and the likes in Real-Time Strategy games. The system consists of a task handler agent that manages all the active and halted tasks. A task is either low-level; used for ordering units, or high-level that can form advanced strategies. The General forms plans that are most beneficial a
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Bjarnolf, Philip. "Threat Analysis Using Goal-Oriented Action Planning : Planning in the Light of Information Fusion." Thesis, University of Skövde, School of Humanities and Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1108.

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<p>An entity capable of assessing its and others action capabilities possess the power to predict how the involved entities may change their world. Through this knowledge and higher level of situation awareness, the assessing entity may choose the actions that have the most suitable effect, resulting in that entity’s desired world state.</p><p>This thesis covers aspects and concepts of an arbitrary planning system and presents a threat analyzer architecture built on the novel planning system Goal-Oriented Action Planning (GOAP). This planning system has been suggested for an application for im
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Gehlin, Rikard. "Action Planning and Cooperation (APAC) between multiple AI-agents." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108865.

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An architecture for actionbased planning and cooperation between multiple AI-agents based on the GOAP-architecture was developed together with a system to be used in advanced AI-courses at Linköping unversity. The architecture was implemented in this system to show the possibilities of our work.
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Lindstam, Tim, and Anton Svensson. "Behavior Based Artificial Intelligence in a Village Environment." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20522.

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Abstract. Autonomous agents, also known as AI agents, are staples in modern video games. They take a lot of roles, everything from being quest-givers in roleplaying games, to opposing forces in action- and shooter games. Crafting an AI that is not only easy to create, but also retains humanlike and believable behavior, has always represented a challenge to the development industry, and has in several cases ended up with open world games using AI systems that limit the AI agents to simple moving patterns. In this thesis, a form of AI systems more commonly used in simulation games such as The Si
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Części książek na temat "Goal-Oriented AI"

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Tsyganok, Vitaliy. "A Goal-Oriented Approach Analytics to Strategic Business Planning." In AI, Analytics and Strategic Decision Making. Routledge, 2025. https://doi.org/10.4324/9781003507840-10.

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Labruna, Tiziano, Sofia Brenna, Andrea Zaninello, and Bernardo Magnini. "Unraveling ChatGPT: A Critical Analysis of AI-Generated Goal-Oriented Dialogues and Annotations." In AIxIA 2023 – Advances in Artificial Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47546-7_11.

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Lorenz, Uwe, and Ralf Romeike. "What Is AI-PACK? – Outline of AI Competencies for Teaching with DPACK." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44900-0_2.

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AbstractThe changes brought by digital transformation have already been addressed in various places for schools and teacher training. Among other things, competence frameworks have been developed for practical orientation and to describe required skills. However, the reflective handling of the now widespread phenomena of the “AI world” requires different competencies than the handling of conventional informatics systems mainly discussed so far, because “AI systems” are based on a different design approach that gives these systems special properties. This, in turn, poses new demands on the prof
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Ködding, Patrick, Christian Koldewey, and Roman Dumitrescu. "Scenario-Based Foresight in the Age of Digital Technologies and AI." In New Digital Work. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26490-0_4.

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AbstractScenario-based foresight is used less and less in the corporate world despite continued high satisfaction with the obtained results. In the age of digitalization, many companies feel increasingly forced to short-termism instead of strategic planning. However, emerging digital technologies, such as artificial intelligence (AI), represent a promising approach to cope with the traditional challenges of scenario-based foresight as well as new challenges added by digitalization. Therefore, this work-in-progress paper identifies and analyzes use cases for scenario-based foresight with digita
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Shneiderman, Ben. "Introduction: What Are the Goals of AI Research?" In Human-Centered AI. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192845290.003.0011.

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Debates rage about the AI goals, with two primary camps. One sees the goal of AI research as long-term curiosity-driven research to understand how computational agents, humans or machines, perceive, decide, and act in the real world. They do their work by building programs that perform well in laboratory studies on specified tasks. The second camp of AI researchers is devoted to creating successful products and services by using many forms of algorithms, especially machine and deep learning. They are devoted to mission-oriented tasks that could be widely deployed.
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Tegon, Rita. "Toward the 4th Agenda 2030 Goal." In Handbook of Research on Teaching With Virtual Environments and AI. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7638-0.ch025.

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The 2030 Agenda settles inclusion as a crucial goal. The index for inclusion underlines a set of resources to guide educational agencies through a process of inclusive development. One interesting model to achieve it is the Universal Design of Learning (UDL) framework, whose roots lie in the field of architecture and cognitive neuroscience. It provides options to enhance the executive functions also with the support of assistive technologies: studies have recently contributed to investigate how AI-innovated Educational Management Information Systems (EMIS), apps, and learning assessments can o
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Thagard, Paul. "Machine Consciousness." In Dreams, Jokes, and Songs. Oxford University PressOxford, 2025. https://doi.org/10.1093/9780198962373.003.0009.

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Abstract This chapter explores the potential for artificial intelligence (AI) to become conscious, using the Neural Representation, Binding, and Coherence (NBC) theory as a framework. It evaluates AI models like ChatGPT, highlighting their strengths in linguistic, abstract reasoning, and multimodal capabilities while recognizing their limitations in sensory, emotional, and goal-oriented consciousness. The chapter explains how AI’s neural network structure differs from biological neural processes, affecting its ability to replicate human-like consciousness. Despite their intelligence, current g
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Gandhi, M., C. Satheesh, Edwin Shalom Soji, M. Saranya, S. Suman Rajest, and Sudheer Kumar Kothuru. "Image Recognition and Extraction on Computerized Vision for Sign Language Decoding." In Explainable AI Applications for Human Behavior Analysis. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1355-8.ch010.

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The image recognition method is a significant process in addressing contemporary global issues. Numerous image detection, analysis, and classification strategies are readily available, but the distinctions between these approaches remain somewhat obscure. Therefore, it is essential to clarify the differences between these techniques and subject them to rigorous analysis. This study utilizes a dataset comprising standard American Sign Language (ASL) and Indian Sign Language (ISL) hand gestures captured under various environmental conditions. The primary objective is to accurately recognize and
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De Giuseppe, Tonia, Silvia Tornusciolo, and Enza paola Catalano. "Sustainable Development Between Artificial Intelligence and Education." In Advances in Business Information Systems and Analytics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3003-6.ch014.

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This chapter, starting from the United Nation's (UN) 2030 agenda, analyzes the current stage of implementing its objectives, focusing on the milestones. The UN Agenda 2030 aims to overcome inequalities, poverty, and disparities. This ambitious goal, especially considering the social transformations caused by the pandemic, requires new models of sustainable development. Sustainability and inclusion are key: how can artificial intelligence (AI) support sustainable development and create an inclusive society? AI's pervasiveness can become a risk without a society democratically oriented toward aw
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Hammad, Rawad. "Artificial Intelligence Enabled Framework for Quality Education: A Mechanism to Leverage SDG Applications." In ACCELERATE THE PACE OF AGENDA 2030 IMPLEMENTATION. World Association for Sustainable Development, 2024. https://doi.org/10.47556/b.outlook2024.22.11.

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PURPOSE: The continuously evolving Artificial Intelligence (AI) oriented artefacts are transforming every facet of daily life, including the education sector. Recognising education’s critical role in enhancing citizens’ quality of life, it becomes essential to explore how AI can revolutionise education, including learning and teaching processes, educational settings, learning outcomes, among other components. However, the complexity and scale of AI developments pose significant challenges to its effective implementation in educational contexts. METHOD: Integrating AI in education has substanti
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Streszczenia konferencji na temat "Goal-Oriented AI"

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Vizzarri, Alessandro. "An Intelligent AI-Based Smart Contract for Goal-Oriented Semantic Network in the 6G Automotive Sector." In 2024 AEIT International Annual Conference (AEIT). IEEE, 2024. http://dx.doi.org/10.23919/aeit63317.2024.10736788.

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Ogundiran, Ayodeji, Hongmei Chi, Jie Yan, and Ruth Agada. "A Framework to Reconstruct Digital Forensics Evidence via Goal-Oriented Modeling." In 2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC). IEEE, 2023. http://dx.doi.org/10.1109/icaic57335.2023.10044180.

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Wang, Ben. "GOLF: Goal-Oriented Long-term liFe tasks supported by human-AI collaboration." In SIGIR 2024: The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2024. http://dx.doi.org/10.1145/3626772.3657655.

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Rajendran, Janarthanan, Jonathan K. Kummerfeld, and Satinder Baveja. "Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks." In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.nlp4convai-1.16.

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Batista, Beatriz Braga, Márcia Sampaio Lima, and Tayana Uchôa Conte. "Teaching Requirements Engineering for AI: A Goal-Oriented Approach in Software Engineering Courses." In SBQS 2024: XXIII Brazilian Symposium on Software Quality. ACM, 2024. https://doi.org/10.1145/3701625.3701686.

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Strinati, Emilio Calvanese, Paolo Di Lorenzo, Vincenzo Sciancalepore, et al. "Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach." In 2024 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit). IEEE, 2024. http://dx.doi.org/10.1109/eucnc/6gsummit60053.2024.10597087.

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Bhende, Veena, Kamna Bhadoriya, Subodh Ghonge, Aryan Kadam, and Yash Kalange. "AI-Based Software Application Planner." In The Second International Conference of AI new Technology and open Discussion. Algorithm Lab, 2025. https://doi.org/10.63211/j.p.25.645302.

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AI planning aims to automate goal-oriented tasks through structured methodologies. While traditional AI tools demonstrate significant capabilities, they often encounter challenges in practical real-world applications. To overcome these limitations, we propose a service-oriented planning architecture that organizes AI planning functionalities into modular and reusable components. Our system automates project architecture design based on user inputs, employing algorithms such as decision trees to recommend optimal technology stacks. Users benefit from customized architectural diagrams that facil
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Sengupta, Sailik, Jason Krone, and Saab Mansour. "On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise." In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI. Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.nlp4convai-1.7.

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Wilder, Bryan. "AI at the Margins: Data, Decisions, and Inclusive Social Impact." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/926.

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Artificial intelligence holds tremendous promise to improve human well-being. However, AI techniques are typically developed for the benefit of those with access to technological and financial resources. A critical but understudied question is how AI can benefit marginalized communities who lack such resources. Governments and communities worldwide use a range of interventions to tackle social problems such as homelessness and disease, improving access to opportunity for underserved populations. My research develops machine learning and optimization methods to empower such interventions, which
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Линявский, А. "Сравнение моделей Игрового Искусственного Интеллекта". У The International Workshop on Information, Computation, and Control Systems for Distributed Environments 2024. Crossref, 2024. http://dx.doi.org/10.47350/iccs-de.2024.32.

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Рассматриваются существующие модели игрового искусственного интеллекта. Приведено краткое описание базовой реализации и работы моделей игрового искусственного интеллекта Finite State Machine (FSM), Behavior Tree, Utility AI, Goal Oriented Action Panner (GOAP), Hierarchical Task Network (HTN). Для каждой модели выделены положительный и отрицательные стороны разработки, дальнейшего поддержания и использования. Произведён анализ и сравнение со стороны рационального агента на основе описания базовой реализации каждой модели.
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