1,721,295 research outputs found
Using an Explicit Teamwork Model and Learning in RoboCup: An Extended Abstract
No full textStacy Marsella, Jafar Adibi, Yaser Al-Onaizan, Ali Erdem, Randall Hill Gal A. Kaminka, Zhun Qiu, Milind Tambe Information Sciences Institute and Computer Science Department University of Southern California 4676 AdmiraltyWay, Marina del Rey, CA 90292, USA [email protected] 1 Introduction The RoboCup research initiative has established synthetic and robotic soccer as testbeds for pursuing researchchallenges in Arti#cial Intelligence and robotics. This extended abstract focuses on teamwork and learning, two of the multiagent researchchallenges highlighted in RoboCup. To address the challenge of teamwork, we discuss the use of a domain-independent explicit model of teamwork, and an explicit representation of team plans and goals. We also discuss the application of agent learning in RoboCup. The vehicle for our researchinvestigations in RoboCup is ISIS #ISI Synthetic#, a team of synthetic soccer-players that successfully participated in the simulation league of RoboCup'97, by..
Coordination Artifacts: Environment-based Coordination for Intelligent Agents
No full textDirect interaction and explicit communication are not always the best approaches for achieving coherent systemic behaviour in the context of Multi-Agent Systems (MAS). This is evident when taking into account recent approaches dealing with environment-based coordination such as stigmergy and, more generally, mediated interaction. In this paper we propose a conceptual, formal and engineering framework based on the notion of coordination artifact, which aims at generally systematising implicit communication and environment-based coordination for heterogeneous, possibly intelligent agents. The features and benefits of our approach are exemplified in the Follow-me situation, where an agent's action/plan is considered as a model for the action/plan of other agents. We model this class of problems in terms of coordination artifacts, from simple to more challenging cases, stressing the advantages with respect to more “standard” MAS approaches
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Towards flexible teamwork
No full textMany AI researchers are today striving to build agent teams for complex, dynamic multi-agent domains, with intended applications in arenas such as education, training, entertainment, information integration, and collective robotics. Unfortunately, uncertain-ties in these complex, dynamic domains obstruct coherent teamwork. In particular, team members often encounter diering, incomplete, and possibly inconsistent views of their en-vironment. Furthermore, team members can unexpectedly fail in fullling responsibilities or discover unexpected opportunities. Highly
exible coordination and communication is key in addressing such uncertainties. Simply tting individual agents with precomputed coordination plans will not do, for their in
exibility can cause severe failures in teamwork, and their domain-specicity hinders reusability. Our central hypothesis is that the key to such
exibility and reusability is providing agents with general models of teamwork. Agents exploit such models to autonomously rea-son about coordination and communication, providing requisite
exibility. Furthermore, the models enable reuse across domains, both saving implementation eort and enforc-ing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in STEAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM is based on agents ' building up a (partial) hierar-chy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial Shared-Plans, 1996). Furthermore, in STEAM, team members monitor the team's and individual members ' performance, reorganizing the team as necessary. Finally, decision-theoretic com-munication selectivity in STEAM ensures reduction in communication overheads of team-work, with appropriate sensitivity to the environmental conditions. This article describes STEAM's application in three dierent complex domains, and presents detailed empirical results. 1
Eliminating expensive chunks by restricting expressiveness
No full textChunking, an experience based-learning mechanism, improves Soar's performance a great deal when viewed in terms of the number of subproblems required and the number of steps within a subproblem. This high-level view of the impact of chunking on performance is based on an deal computational model, which says that the time per step is constant. However, if the chunks created by chunking are expensive, then they consume a large amount of processing in the match, i.e, indexing the knowledge-base, distorting Soar*s constant time-per-stcp model. In these situations, the gain in number of steps does not reflect an improvement in performance; in fact there may be degradation in the total run time of the system. Such chunks form a major problem for the system, since absolutely 10 guarantees can be given about its behavior. I "his article presents a solution to the problem of expensive chunks. The solution is based on the notion of restricting the expressiveness of Soar's representational language to guarantee that chunks formed will require only a limited amount of matching effort. We analyze the tradeoffs involved in restricting expressiveness and present some empirical evidence to support our analysis. 1
Towards Algorithmic Advances for Solving Stackelberg Games: Addressing Model Uncertainties and Massive Game Scale-up
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