352 research outputs found

    Non-Monotonic Reasoning on Board a Sony AIBO

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    Griffith Sciences, School of Information and Communication TechnologyFull Tex

    All for One and One for All:: How Teams Adapt to Crises

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    All for One and One for All:: How Teams Adapt to Crises

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    Computational Theory of Mind for Human-Agent Coordination

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    In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.</p

    The Impact of Teams in Multiagent Systems

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    Across many domains, the ability to work in teams can magnify a group's abilities beyond the capabilities of any individual. While the science of teamwork is typically studied in organizational psychology (OP) and areas of biology, understanding how multiple agents can work together is an important topic in artificial intelligence (AI) and multiagent systems (MAS). Teams in AI have taken many forms, including ad hoc teamwork [Stone et al., 2010], hierarchical structures of rule-based agents [Tambe, 1997], and teams of multiagent reinforcement learning (MARL) agents [Baker et al., 2020]. Despite significant evidence in the natural world about the impact of family structure on child development and health [Lee et al., 2015; Umberson et al., 2020], the impact of team structure on the policies that individual learning agents develop is not often explicitly studied. In this thesis, we hypothesize that teams can provide significant advantages in guiding the development of policies for individual agents that learn from experience. We focus on mixed-motive domains, where long-term global welfare is maximized through global cooperation. We present a model of multiagent teams with individual learning agents inspired by OP and early work using teams in AI, and introduce credo, a model that defines how agents optimize their behavior for the goals of various groups they belong to: themselves (a group of one), any teams they belong to, and the entire system. We find that teams help agents develop cooperative policies with agents in other teams despite game-theoretic incentives to defect in various settings that are robust to some amount of selfishness. While previous work assumed that a fully cooperative population (all agents share rewards) obtain the best possible performance in mixed-motive domains [Yang et al., 2020; Gemp et al., 2020], we show that there exist multiple configurations of team structures and credo parameters that achieve about 33% more reward than the fully cooperative system. Agents in these scenarios learn more effective joint policies while maintaining high reward equality. Inspired by these results, we derive theoretical underpinnings that characterize settings where teammates may be beneficial, or not beneficial, for learning. We also propose a preliminary credo-regulating agent architecture to autonomously discover favorable learning conditions in challenging settings

    Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

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    http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

    Exploration of Sensemaking in the Education of Novices to the Complex Cognitive Work Domain of Air Traffic Control

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    Many current complex business and industry jobs consist primarily of cognitive work; however, current approaches to training may be inadequate for this type of work (Hoffman, Feltovich, Fiore, Klein, & Ziebell, 2009). To try and improve training and education for cognitive work, Klein and Baxter (2006) have proposed cognitive transformation theory (CTT), a learning theory that claims that sensemaking activities are essential for acquiring expertise that is adaptive and thus well suited for cognitive work domains. In the present research, cognitive task analysis methods were used to identify and assess sensemaking support in the instruction and learning of complex concepts by two experienced air traffic control professors and seven of their students. The goal of this research was to compare instructional strategies used in an academic setting with the predictions of CTT to gain insight into strategies for the application of CTT. Cognitive task analysis methods employed included course observation, artifact examination, and knowledge elicitation sessions with two professors and seven of their students. Knowledge elicitation transcriptions were coded using categories derived from CTT and the data/frame theory of sensemaking (e.g. Klein, Moon, & Hoffman, 2006; Sieck, Klein, Peluso, Smith, & Harris-Thompson, 2007) to assess theoretical and applied implications for learning and instruction in a complex domain. Findings are represented by synthesizing theory driven predictions with grounded training strategies and technologies. In addition, recommendations are advanced for applying CTT to training and educational systems in order to provide sensemaking support during early phases of learning from which expertise may be developed

    Voting Without Law

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    Voting Without Law

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    Multiagent reactive plan application learning in dynamic environments

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