S. Noh and P. J. Gmytrasiewicz. Towards flexible multiagent decision-making under time pressure. In T. Dean, editor, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 492--498. Morgan Kaufmann, San Francisco, CA, 1999. Home/Search Document Details and Download
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Systems That Adapt to Their Users - Description of an IJCAI 01.. - Jameson (Correct)
....user adaptive systems have typically been represented in several different sessions at past IJCAI conferences. For example, at IJCAI99 they could be found in part of the invited talk by David Heckerman on learning Bayesian networks; in sessions on Cognitive Modeling ( 1] Multi Agent Systems ([3]) Challenge Papers ( 4] Learning for Information Retrieval ( 2] and Probabilistic Reasoning and Learning ( 5] and in workshops, including the one devoted to Learning About Users (http: www.sics.se humle ijcai99 ws ) This distribution of relevant contributions at IJCAI conferences shows ....
S. Noh and P. J. Gmytrasiewicz. Towards flexible multiagent decision-making under time pressure. In T. Dean, editor, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 492--498. Morgan Kaufmann, San Francisco, CA, 1999.
Meeting Plan Recognition Requirements for Real-Time Air-Mission.. - At Io Ns (2000) (Correct)
....are multiple entities operating in the simulation. Recently, relationships between agents have been used to advantage in goal attentive schemes [5] A recursive modelling method has also been used for representing uncertain knowledge and communicative behaviour in coordinated defence campaigns [6]. Our multi agent flight simulations have involved either closed loop or pilot in the loop simulations in the Air Operations Simulation Centre (AOSC) six video projector DOME display facility. Plan recognition and means end reasoning is used to explore outcomes based on tactical procedures, ....
Noh, S. and Gmytrasiewicz, P. J. Towards Flexible MultiAgent Decision-Making Under Time Pressure Proc.. of the International Joint Conference on Artificial Intelligence, pp 492-498, (1999)
Using Decision Theory to Formalize Emotions for.. - Gmytrasiewicz, Lisetti (2000) (2 citations) Self-citation (Gmytrasiewicz) (Correct)
....that a mechanism for managing the agent s computational resources is needed. Here, we suggest that emotional states, as defined below, may provide for such ability. Other approaches, such as compilations of decision theoretic reasoning, approximations, and simplifications were investigated in [21]. 3 Emotional States: Classification and Dynamics As we mentioned, we will view emotions as transformations of the decision making situation defined above. First, we briefly describe a taxonomy of emotional states and a finite state machine model of dynamics of emotions which can assist agents ....
Sanguk Noh and Piotr J. Gmytrasiewicz. Towards flexible multi-agent decision-making under time pressure. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 492--498, August 1999. 10
Rational Coordination in Multi-Agent Environments - Gmytrasiewicz, Durfee (1999) (5 citations) Self-citation (Gmytrasiewicz) (Correct)
....of the recursive model structure, and l is the level of nesting of the model structure. Luckily, an exhaustive evaluation of the full blown RMM hierarchy can be simplified in a 26 number of ways. For lack of space, we briefly list some of the most intuitive methods (see [27] and the more recent [58] for more details) First, the dynamic programming solution of the recursive model structure takes advantage of the property of overlapping subproblems (see [18] section 16.2) which avoids repeated redundant solutions of branches with the same form in the recursive model structure. The extent to ....
....for example, the building, organization, or the society at large. As it turns out, the payoff matrices lend themselves to an efficient assessment of the strength of interaction between agents by analyzing variability of the payoff values. For details of these and other simplification methods, see [27, 58, 77], and related work in [60, 69] 8 Summary and Conclusions The starting point for our explorations in this paper has been the presumption that coordination should emerge as a result of rational decisions in multi agent situations, where we defined rationality as maximization of expected utility. ....
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Sanguk Noh and Piotr J. Gmytrasiewicz. Towards flexible multi-agent decision-making under time pressure. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 492--498, August 1999. 32
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