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Journal articles on the topic 'Search agents'

Author: Grafiati

Published: 19 February 2023

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1

HAMADI, YOUSSEF. "CONFLICTING AGENTS IN DISTRIBUTED SEARCH." International Journal on Artificial Intelligence Tools 14, no. 03 (June 2005): 459–75. http://dx.doi.org/10.1142/s021821300500220x.

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We extend here the work on interleaved distributed graph based backjumping, IDIBT/GBJ7,6,5 by considering conflicting agents.13 The resulting method IDIBT/CBJ-DkC for Conflict-directed Back-Jumbing and Directed k-Consistency combines distributed and parallel explorations of search spaces with more efficient backtrack steps. We take advantage of the asynchronous framework to implement a more refined and efficient update of conflicts. A fine analysis of these conflicts allows to some extent the detection of k-inconsistent values. Our experiments show a large improvement over graph based distributed/interleaved search.

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2

Noolvi, Malleshappa N., Harun M. Patel, and Manpreet Kaur. "Benzothiazoles: Search for anticancer agents." European Journal of Medicinal Chemistry 54 (August 2012): 447–62. http://dx.doi.org/10.1016/j.ejmech.2012.05.028.

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3

Dash, BisnuPrasad, Y. Archana, Nibarana Satapathy, and SoumendraKumar Naik. "Search for antisickling agents from plants." Pharmacognosy Reviews 7, no. 1 (2013): 53. http://dx.doi.org/10.4103/0973-7847.112849.

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4

Whatley, Stephen A. "Agents in Search of a Role." Contemporary Psychology: A Journal of Reviews 35, no. 1 (January 1990): 78–79. http://dx.doi.org/10.1037/028194.

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5

Ugale, Vinod, Harun Patel, Bijal Patel, and Sanjay Bari. "Benzofurano-isatins: Search for antimicrobial agents." Arabian Journal of Chemistry 10 (February 2017): S389—S396. http://dx.doi.org/10.1016/j.arabjc.2012.09.011.

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6

Jansen, Bernard J., and Udo Pooch. "Assisting the searcher: utilizing software agents for Web search systems." Internet Research 14, no. 1 (February 2004): 19–33. http://dx.doi.org/10.1108/10662240410516291.

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7

Atzmon, Dor, Jiaoyang Li, Ariel Felner, Eliran Nachmani, Shahaf Shperberg, Nathan Sturtevant, and Sven Koenig. "Multi-Directional Search." Proceedings of the International Symposium on Combinatorial Search 11, no. 1 (September 1, 2021): 121–22. http://dx.doi.org/10.1609/socs.v11i1.18518.

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In the Multi-Agent Meeting (MAM) problem, the task is to find a meeting location for multiple agents, as well as a path for each agent to that location. In this paper, we introduce MM*, a Multi-Directional Search algorithm that finds the optimal meeting location under different cost functions. MM* generalizes the Meet in the Middle (MM) bidirectional search algorithm to the case of finding optimal meeting locations for multiple agents. A number of admissible heuristics are proposed and experiments demonstrate the benefits of MM*.

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8

Sarne, David, Simon Shamoun, and Eli Rata. "Increasing Threshold Search for Best-Valued Agents." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 848–53. http://dx.doi.org/10.1609/aaai.v24i1.7628.

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This paper investigates search techniques for multi-agent settings in which the most suitable agent, according to given criteria, needs to be found. In particular, it considers the case where the searching agent incurs a cost for learning the value of an agent and the goal is to minimize the expected overall cost of search by iteratively increasing the extent of search. This kind of search is applicable to various domains, including auctions, first responders, and sensor networks. Using an innovative transformation of the extents-based sequence to a probability-based one, the optimal sequence is proved to consist of either a single search iteration or an infinite sequence of increasing search extents. This leads to a simplified characterization of the the optimal search sequence from which it can be derived. This method is also highly useful for legacy economic-search applications, where all agents are considered suitable candidates and the goal is to optimize the search process as a whole. The effectiveness of the method for both best-valued search and economic search is demonstrated numerically using a synthetic environment.

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9

DRAGANOV STOJANOVSKI, Toni. "Performance of exhaustive search with parallel agents." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 22 (2014): 1382–94. http://dx.doi.org/10.3906/elk-1210-105.

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10

Tripathi, Rama Pati, Diksha Katiyar, Namrata Dwivedi, Biswajit Singh, and Jyoti Pandey. "Recent Developments in Search of Antifilarial Agents." Current Medicinal Chemistry 13, no. 27 (November 1, 2006): 3319–34. http://dx.doi.org/10.2174/092986706778773103.

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11

Hong, Seung‐Hyun. "Real estate agents' influence on housing search." Journal of Applied Econometrics 37, no. 3 (January 27, 2022): 563–82. http://dx.doi.org/10.1002/jae.2891.

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12

Churchill, David, and Michael Buro. "Incorporating Search Algorithms into RTS Game Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 8, no. 3 (June 30, 2021): 2–7. http://dx.doi.org/10.1609/aiide.v8i3.12548.

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Real-time strategy (RTS) games are known to be one of the most complex gamegenres for humans to play, as well as one of the most difficult games forcomputer AI agents to play well. To tackle the task of applying AI to RTSgames, recent techniques have focused on a divide-and-conquer approach,splitting the game into strategic components, and developing separate systemsto solve each. This trend gives rise to a new problem: how to tie thesesystems together into a functional real-time strategy game playing agent. Inthis paper we discuss the architecture of UAlbertaBot, our entry into the 2011/2012 StarCraft AI competitions, and the techniques used to include heuristic search based AI systems for the intelligent automation of both build order planning and unit control for combat scenarios.

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13

Pant, G., O. P. Sati, K. Miyahara, and T. Kawasaki. "Search for Molluscicidal Agents: Saponins fromAgave cantalaLeaves." International Journal of Crude Drug Research 25, no. 1 (January 1987): 35–38. http://dx.doi.org/10.3109/13880208709060908.

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14

Lantz, John M. "IN SEARCH OF AGENTS FOR SELF-CARE." Journal of Gerontological Nursing 11, no. 7 (July 1, 1985): 10–14. http://dx.doi.org/10.3928/0098-9134-19850701-06.

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15

Sawaya, Marty E. "Alopecia - the search for novel agents continues." Expert Opinion on Therapeutic Patents 7, no. 8 (August 1997): 859–72. http://dx.doi.org/10.1517/13543776.7.8.859.

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16

Koltin, Yigal, and Christopher A. Hitchcock. "The search for new triazole antifungal agents." Current Opinion in Chemical Biology 1, no. 2 (August 1997): 176–82. http://dx.doi.org/10.1016/s1367-5931(97)80007-5.

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17

Nirmala, C. R., N. M. Jyothi, and Dr V. Ramaswamy. "Mobile Agents for Audio Search & Retrieval." International Journal of Computer Applications 1, no. 23 (February 25, 2010): 77–82. http://dx.doi.org/10.5120/532-695.

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18

Shamoun, Simon, and David Sarne. "Increasing threshold search for best-valued agents." Artificial Intelligence 199-200 (June 2013): 1–21. http://dx.doi.org/10.1016/j.artint.2013.03.002.

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19

Mueller-Frank, Manuel, and Mallesh M. Pai. "Social Learning with Costly Search." American Economic Journal: Microeconomics 8, no. 1 (February 1, 2016): 83–109. http://dx.doi.org/10.1257/mic.20130253.

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We study a sequential social learning model where agents privately acquire information by costly search. Search costs of agents are private, and are independently and identically distributed. We show that asymptotic learning occurs if and only if search costs are not bounded away from zero. We explicitly characterize equilibria for the case of two actions, and show that the probability of late moving agents taking the suboptimal action vanishes at a linear rate. Social welfare converges to the social optimum as the discount rate converges to one if and only if search costs are not bounded away from zero. (JEL D81, D83)

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20

Jin, Hong Ying. "Design of Intelligent Search Engine with Multiple Agents." Applied Mechanics and Materials 496-500 (January 2014): 1937–40. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1937.

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Because traditional search engine has lots of defect such as return too much information or search result has little use, it is necessary to study on intelligent search engine. Base on the analysis of general search engine defects, this paper presents an intelligent search engine with multiple agent. Then, the structure and search process was discussed in detail. At last, the superiority of the new scheme was analyzed compared with the general search engine.

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21

Antler, Yair, and Benjamin Bachi. "Searching Forever After." American Economic Journal: Microeconomics 14, no. 3 (August 1, 2022): 558–90. http://dx.doi.org/10.1257/mic.20200020.

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We study a model of two-sided search in which agents’ strategic reasoning is coarse. In equilibrium, the most desirable agents behave as if they were fully rational, while for all other agents, coarse reasoning results in overoptimism with regard to their prospects in the market. Consequently, they search longer than is optimal. Moreover, agents with intermediate match values may search indefinitely while all other agents eventually marry. We show that the share of eternal singles converges monotonically to one as search frictions vanish. Thus, improvements in search technology may backfire and even lead to market failure. (JEL C78, D11, D83, J12)

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22

Menczer, Filippo, Le-Shin Wu, and Ruj Akavipat. "Intelligent Peer Networks for Collaborative Web Search." AI Magazine 29, no. 3 (September 6, 2008): 35. http://dx.doi.org/10.1609/aimag.v29i3.2155.

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Collaborative query routing is a new paradigm for Web search that treats both established search engines and other publicly available indices as intelligent peer agents in a search network. The approach makes it transparent for anyone to build their own (micro) search engine, by integrating established Web search services, desktop search, and topical crawling techniques. The challenge in this model is that each of these agents must learn about its environment— the existence, knowledge, diversity, reliability, and trustworthiness of other agents — by analyzing the queries received from and results exchanged with these other agents. We present the 6S peer network, which uses machine learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and high-quality search results. A prototype of 6S is available for public use and is intended to assist in the evaluation of different AI techniques employed by the networked agents.

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23

GODOY, DANIELA, and ANALÍA AMANDI. "COLLABORATIVE WEB SEARCH BASED ON USER INTEREST SIMILARITY." International Journal of Cooperative Information Systems 17, no. 04 (December 2008): 495–521. http://dx.doi.org/10.1142/s0218843008001907.

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The motivation behind personal information agents resides in the enormous amount of information available on the Web, which has created a pressing need for effective personalized techniques. In order to assists Web search these agents rely on user profiles modeling information preferences, interests and habits that help to contextualize user queries. In communities of people with similar interests, collaboration among agents fosters knowledge sharing and, consequently, potentially improves the results of individual agents by taking advantage of the knowledge acquired by other agents. In this paper, we propose an agent-based recommender system for supporting collaborative Web search in groups of users with partial similarity of interests. Empirical evaluation showed that the interaction among personal agents increases the performance of the overall recommender system, demonstrating the potential of the approach to reduce the burden of finding information on the Web.

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24

Carvalhal, Carlos, Sérgio Deusdado, and Leonel Deusdado. "Crawling PubMed with web agents for literature search and alerting services." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 2, no. 1 (May 7, 2013): 19–22. http://dx.doi.org/10.14201/adcaij2013241922.

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In this paper we present ASAP - Automated Search with Agents in PubMed, a web-based service aiming to manage and automate scientific literature search in the PubMed database. The system allows the creation and management of web agents, parameterized thematically and functionally, that crawl the PubMed database autonomously and periodically, aiming to search and retrieve relevant results according the requirements provided by the user. The results, containing the publications list retrieved, are emailed to the agent owner on a weekly basis, during the activity period defined for the web agent. The ASAP service is devoted to help researchers, especially from the field of biomedicine and bioinformatics, in order to increase their productivity, and can be accessed at: http://esa.ipb.pt/~agentes.

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25

Cheng, Chao, and Wheeler Ruml. "Real-Time Heuristic Search in Dynamic Environments." Proceedings of the International Symposium on Combinatorial Search 10, no. 1 (September 1, 2021): 168–69. http://dx.doi.org/10.1609/socs.v10i1.18474.

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In dynamic environments, agents often do not have time to find a complete plan to reach a goal state, but rather must act quickly under changing circumstances. Real-time heuristic search models this setting by requiring that the agent's next action must be selected within a prespecified time bound. In this paper, we study real-time search algorithms that can tolerate a dynamic environment, in which action costs are not fully predictable. We propose a combination of two previously-proposed methods and study its behavior both theoretically and empirically on several different benchmark domains.

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26

McKaughan, Larry. "The Search for Agents in a Causal Gap." American Journal of Psychology 117, no. 1 (2004): 19. http://dx.doi.org/10.2307/1423594.

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27

Hardeland, Rüdiger. "Neuroprotection by Radical Avoidance: Search for Suitable Agents." Molecules 14, no. 12 (December 7, 2009): 5054–102. http://dx.doi.org/10.3390/molecules14125054.

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28

Kubo, Masao, Nhuhai Phung, and Hiroshi Sato. "Efficient collective search by agents that remember failures." Journal of Robotics, Networking and Artificial Life 5, no. 1 (2018): 67. http://dx.doi.org/10.2991/jrnal.2018.5.1.15.

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29

Batalha, Pedro, Maria Cecília Vieira de Souza, Eduardo Peña-Cabrera, David Cruz, and Fernanda da Santos Boechat. "Quinolones in the Search for New Anticancer Agents." Current Pharmaceutical Design 22, no. 39 (December 14, 2016): 6009–20. http://dx.doi.org/10.2174/1381612822666160715115025.

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30

Patience, O. Osadebe, U. Odoh Estella, and F. Uzor Philip. "The search for new hypoglycemic agents from plants." African Journal of Pharmacy and Pharmacology 8, no. 11 (March 22, 2014): 292–303. http://dx.doi.org/10.5897/ajpp2014.3933.

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31

Cakir, Armagan, and Faruk Polat. "Coordination of intelligent agents in real-time search." Expert Systems 19, no. 2 (May 2002): 80–87. http://dx.doi.org/10.1111/1468-0394.00193.

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32

Buckley, R., and R. D. DiMarchi. "Novel Oral Agents: The Search for Transformational Medicines." MD Conference Express 12, no. 16 (November 1, 2012): 26–27. http://dx.doi.org/10.1177/155989771216013.

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33

García-García, Angeles, Jorge Gálvez, Jesus Vicente de Julián-Ortiz, Ramón García-Domenech, Carlos Muñoz, Remedios Guna, and Rafael Borrás. "Search of Chemical Scaffolds for Novel Antituberculosis Agents." Journal of Biomolecular Screening 10, no. 3 (April 2005): 206–14. http://dx.doi.org/10.1177/1087057104273486.

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A method to identify chemical scaffolds potentially active against Mycobacterium tuberculosis is presented. The molecular features of a set of structurally heterogeneous antituberculosis drugs were coded by means of structural invariants. Three techniques were used to obtain equations able to model the antituberculosis activity: linear discriminant analysis, multilinear regression, and shrinkage estimation–ridge regression. The model obtained was statistically validated through leave- n-out test, and an external set and was applied to a database for the search of new active agents. The selected compounds were assayed in vitro, and among those identified as active stand reserpine, N,N,N′,N′-tetrakis-(2-pyridylmethyl)-ethylenediamine (TPEN), trifluoperazine, pentamidine, and 2-methyl-4,6-dinitro-phenol (DNOC). They show activity comparable to or superior to ethambutol, used in combination with other drugs for the prevention and treatment of Mycobacterium avium complex and drug-resistant tuberculosis.

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34

Cruquenaire, A. "Electronic Agents as Search Engines: Copyright related aspects." International Journal of Law and Information Technology 9, no. 3 (September 1, 2001): 327–43. http://dx.doi.org/10.1093/ijlit/9.3.327.

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35

De Clercq, Erik, and Lieve Naesens. "In search of effective anti-HHV-6 agents." Journal of Clinical Virology 37 (December 2006): S82—S86. http://dx.doi.org/10.1016/s1386-6532(06)70017-8.

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36

Menczer, Filippo. "Complementing search engines with online web mining agents." Decision Support Systems 35, no. 2 (May 2003): 195–212. http://dx.doi.org/10.1016/s0167-9236(02)00106-9.

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37

Pérez, Lucy, and Samuel J. Danishefsky. "Chemistry and Biology in Search of Antimetastatic Agents." ACS Chemical Biology 2, no. 3 (March 2007): 159–62. http://dx.doi.org/10.1021/cb7000395.

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38

Mansilla, Albert Trias i., Sam Sethserey, and Josep Lluís de la Rosa i. Esteva. "Applying Short-term Memory to Social Search Agents." Procedia Computer Science 68 (2015): 217–26. http://dx.doi.org/10.1016/j.procs.2015.09.237.

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39

RAWLS, REBECCA. "Search for More Metal-Containing Antitumor Agents Intensifies." Chemical & Engineering News 64, no. 40 (October 6, 1986): 21–22. http://dx.doi.org/10.1021/cen-v064n040.p021.

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40

Babu, Konda Ramesh, Begari Eeshwaraiah, Dachepally Aravind, Harshadas M. Meshram, Rallabaldi Madhusudan Raju, Apurba Bhattacharya, and Rakeshwar Bandichhor. "Synthesis of Quinoline Analogs: Search for Antimalarial Agents." Monatshefte für Chemie - Chemical Monthly 139, no. 2 (December 24, 2007): 179–81. http://dx.doi.org/10.1007/s00706-007-0772-5.

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41

Kubo, Masao, Nhuhai Phung, and Hiroshi Sato. "Efficient collective search by agents that remember failures." Proceedings of International Conference on Artificial Life and Robotics 23 (February 2, 2018): 93–95. http://dx.doi.org/10.5954/icarob.2018.gs3-3.

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42

Plakunov, V. K., M. V. Zhurina, A. V. Gannesen, S. V. Mart’yanov, and Yu A. Nikolaev. "Antibiofilm Agents: Therminological Ambiguity and Strategy for Search." Microbiology 88, no. 6 (November 2019): 747–50. http://dx.doi.org/10.1134/s0026261719060146.

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43

Andrasi, Ferenc. "ChemInform Abstract: Search for Antiulcer Agents at IDR." ChemInform 32, no. 50 (May 23, 2010): no. http://dx.doi.org/10.1002/chin.200150273.

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44

Catalano, Alessia, Alessia Carocci, Ivana Defrenza, Marilena Muraglia, Antonio Carrieri, Françoise Van Bambeke, Antonio Rosato, Filomena Corbo, and Carlo Franchini. "2-Aminobenzothiazole derivatives: Search for new antifungal agents." European Journal of Medicinal Chemistry 64 (June 2013): 357–64. http://dx.doi.org/10.1016/j.ejmech.2013.03.064.

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45

Sarne, David, and Sarit Kraus. "Managing parallel inquiries in agents' two-sided search." Artificial Intelligence 172, no. 4-5 (March 2008): 541–69. http://dx.doi.org/10.1016/j.artint.2007.09.002.

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46

Kagan, T. I., M. Yu Lidak, D. V. Meyrena, and B. Z. Simkhovich. "Search for antitumor agents among synthetic polycations (survey)." Pharmaceutical Chemistry Journal 22, no. 6 (June 1988): 413–21. http://dx.doi.org/10.1007/bf00763369.

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47

Kaneko, Tomoyuki. "Parallel Depth First Proof Number Search." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 95–100. http://dx.doi.org/10.1609/aaai.v24i1.7551.

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The depth first proof number search (df-pn) is an effective and popular algorithm for solving and-or tree problems by using proof and disproof numbers. This paper presents a simple but effective parallelization of the df-pn search algorithm for a shared-memory system. In this parallelization, multiple agents autonomously conduct the df-pn with a shared transposition table. For effective cooperation of agents, virtual proof and disproof numbers are introduced for each node, which is an estimation of future proof and disproof numbers by using the number of agents working on the node's descendants as a possible increase. Experimental results on large checkmate problems in shogi, which is a popular chess variant in Japan, show that reasonable increases in speed were achieved with small overheads in memory.

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48

Aoki, Masanao, and Yoshimasa Shirai. "A NEW LOOK AT THE DIAMOND SEARCH MODEL: STOCHASTIC CYCLES AND EQUILIBRIUM SELECTION IN SEARCH EQUILIBRIUM." Macroeconomic Dynamics 4, no. 4 (December 2000): 487–505. http://dx.doi.org/10.1017/s1365100500017041.

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We recast Diamond's search equilibrium model into that with a finite number of agents. The state of the model is described by a jump-Markov process, the transition rates of which are functions of the reservation cost, which are endogenously determined by value maximization by rational agents. The existence of stochastic fluctuations causes the fraction of the employed to move from one basin of attraction to the other with positive probabilities when the dynamics have multiple equilibria. Stochastic asymmetric cycles that arise are quite different from the cycles of the set of Diamond–Fudenberg nonlinear deterministic differential equations. By taking the number of agents to infinity, we get a limiting probability distribution over the stationary state equilibria. This provides a natural basis for equilibrium selection in models with multiple equilibria, which is new in the economic literature.

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49

Abeywickrama, Tenindra, Muhammad Aamir Cheema, and Sabine Storandt. "Hierarchical Graph Traversal for Aggregate k Nearest Neighbors Search in Road Networks." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 2–10. http://dx.doi.org/10.1609/icaps.v30i1.6639.

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Location-based services rely heavily on efficient methods that search for relevant points-of-interest (POIs) close to a given location. A k nearest neighbors (kNN) query is one such example that finds k closest POIs from an agent's location. While most existing techniques focus on finding nearby POIs for a single agent, many applications require POIs that are close to multiple agents. In this paper, we study a natural extension of the kNN query for multiple agents, namely, the Aggregate k Nearest Neighbors (AkNN) query. An AkNN query retrieves k POIs with the smallest aggregate distances where the aggregate distance of a POI is obtained by aggregating its distances from the multiple agents (e.g., sum of its distances from each agent). Existing search heuristics are designed for a single agent and do not work well for multiple agents. We propose a novel data structure COLT (Compacted Object-Landmark Tree) to address this gap by enabling efficient hierarchical graph traversal. We then utilize COLT for a wide range of aggregate functions to efficiently answer AkNN queries. In our experiments on real-world and synthetic data sets, our techniques significantly improve query performance, typically outperforming existing approaches by more than an order of magnitude in almost all settings.

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50

Roger, Guillaume, and Benoît Julien. "Moral Hazard and Efficiency in a Frictional Market." American Economic Journal: Microeconomics 15, no. 1 (February 1, 2023): 693–730. http://dx.doi.org/10.1257/mic.20200378.

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Principals seek to trade with homogeneous agents by posting incentive contracts, which direct their search. Search and moral hazard interact in equilibrium. If using transfers to compensate agents failing to contract, the equilibrium allocation is always constrained-welfare-optimal in contrast to the one-to-one principal-agent problem. Search frictions thus correct that inefficiency because search requires internalizing the utility of agents. Incentives are weaker than in bilateral contracting, and agents enjoy more efficient risk sharing. With a constraint on transfers the allocation may become inefficient; principal competition results in overinsurance of the agents, too little effort in equilibrium, and excessive entry by principals. (JEL D82, D83, D86)

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