Gmytrasiewicz, P. and E. Durfee, (ms), Rational Interactions in Multiagent Environments, University of Texas and University of Michigan.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....on the communication and coordination of cooperative agents, with the goal of nding best policy tuples and achieving the highest global reward. We directly model multi agent problem solving and communication into a decision process. This is di erent from the work by Gmytrasiewicz and Durfee [4], which considers agent decision making from the perspective of an individual agent in a self interested environment. In doing so, an agent must maintain its models of the other agents, which can including their models of other agents as well. This creates a recursion and hence the need of a ....

P. J. Gmytrasiewicz and E. H. Durfee. Rational interaction in multiagent environments: Coordination. Autonomous Agents and Multi-Agent Systems Journal, 1999.

....on the communication and coordination of cooperative agents, with the goal of finding best policy tuples. and achieving the highest global reward. We directly model multi agent problem solving and communication into a decision process. This is different from the work by Gmytrasiewicz and Durfee [4], which considers agent decision making from the perspective of an individual agent in a self interested environment. In doing so, an agent must maintain its models of the other agents, which can including their models of other agents as well. This creates a recursion and hence the need of a ....

Piotr J. Gmytrasiewicz and Edmond H. Durfee. Rational interaction in multiagent environments: Coordination. Autonomous Agents and Multi-Agent Systems Journal, 1999.

....the agent has the opportunity to evaluate the goodness of his action not only from a local point of view, but from a state which includes the consequences due to the behavior of other agents. In particular, this form of reasoning has been proven useful in cases of cooperation among agents as in [23], 5] and [4] On the other hand, the recursive modeling of the normative agent opens the way to another opportunity for the bearer besides a better evaluation of the resulting nal state. The bearer agent can reason about how the normative agent will (decide to) check the ful llment and will ....

P. J. Gmytrasiewicz and E. H. Durfee. Rational interaction in multiagent environments: Communication. In Submitted for publication, available at http://www-cse.uta.edu/~piotr/www/piotr.html, 1997.

....agent adapts its reactive component, it should communicate (e.g. broadcast) information about this adaptation to the other agents. In this way we are still able to make linear anticipations. This approach can be contrasted with the Recursive Modeling Method suggested by Gmytrasiewicz and Durfee [8] in which an agent modeling another agent includes that agent s models of other agents and so on, resulting in a recursive nesting of models. 4 Related Work The main task of the Anticipator is to avoid undesired states whereas the main task of the Reactor is to reach the desired state(s) In ....

P.J. Gmytrasiewicz and E.H. Durfee. Rational interaction in multiagent environment: Coordination. (submitted for publication), 1996.

.... rational actions do not adversely affect the overall system efficiency [1] Effective coordination among agents in dynamic environments may be achieved by extending the agents learning ability to recognize the capabilities, desires, and beliefs of other agents present in their environment [6]. Several papers have reported variety of techniques for constructing the models of agents. 10] described a rulebased model for plan recognition task in the air combat simulation environment, while [2] explored the use of finite automata to model the opponent agent s strategy. A series of papers ....

P. J. Gmytrasiewicz and E. H. Durfee. Rational interaction in multiagent environments: coordination. Submitted for publication, available at http://www-cse.uta.edu/ piotr/piotr.html, 1998.

.... To overcome this, several researchers have proposed providing the agents with a general model Supported by Overseas Postgraduate Research Scholarship (OPRS) and Curtin University Postgraduate Scholarship (CUPS) of coordination as the key to flexible and reusable coordination mechanism [8, 17]. Such a general model can be applied to different coordination scenarios, allowing the agents to autonomously reason and search for their optimal course of actions. In this paper, we focus on developing a general model of coordination for team of agents under incomplete information. A group of ....

....in which a team of surrogate agents negotiate to schedule meetings on behalf of their users. Several related general models of coordination have been proposed, most notable are the logics based joint intentions framework [4, 12, 17] and the decisiontheoretic Recursive Modelling Method (RMM) [7, 8, 18]. The latter is similar to our approach in its representation of actions, utility and uncertainties, however differs subtly in the basic assumptions made. While our approach is limited to agents in a team, RMM is applicable mainly in the case where the agents might have conflict of interests. In ....

Piotr J. Gmytrasiewicz and Edmund H. Durfee. Rational interaction in multiagent environments: Coordination. In submission, 1997.

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Gmytrasiewicz, P. J., and Durfee, E. H. (1996b). Rational interaction in multiagent environments: Communication. Submitted for publication. Available in postscript from http://www-cse.uta.edu/ piotr/piotr.html.

.... do not adversely affect the overall system efficiency (Bond and Gasser, 1988) Effective coordination among agents in dynamic environments may be achieved by extending the agents learning ability to recognize the capabilities, desires, and beliefs of other agents present in their environment (Gmytrasiewicz and Durfee, 1998). Several papers have reported variety of techniques for constructing the models of agents. Kaminka et al. 1998) described a rule based model for plan recognition task in the air combat simulation environment, while Carmel and Markovitch (1996) explored the use of finite automata to model the ....

Gmytrasiewicz, P. J., and Durfee, E. H. (1998). Rational interaction in multiagent environments: coordination. Submitted for publication, available at http://www-cse.uta.edu/ piotr/piotr.html.

....its decision theoretic (DT) pragmatics, defined as the transformation of the state of knowledge about the decision making situation (i.e. the recursive model structure) the act brings about. We model DT pragmatics using the RMM representation to investigate the utility of the communicative acts [6, 5]. 3 The transformation in the agent s decision making situation, as represented by RMM s recursive model structure, may change the expected utilities of alternative actions. It is natural to identify the change of the expected utility brought about by a communicative action as the expected ....

....after executing the act: U(M) U p M (Y ) Gamma U p (X) 2) where U p (X) is the utility of the best action, X , expected before sending the message, and U p M (Y ) is the utility of the best action, Y , expected if the message were to be sent. Further details and discussion is contained in [6, 5]. We now apply DT pragmatics to our anti air defense domain. 3.1 Intentional and Modeling Messages Our implementation of communicating autonomous agents is closely related to BDI theories that describe the agent s Beliefs, Desires, and Intentions [2, 20] In a coordinated multi agent ....

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P. J. Gmytrasiewicz and E. H. Durfee. Rational interaction in multiagent environments: Communication. Submitted for publication, 1997. Available in postscript from http://wwwcse. uta.edu/¸piotr/piotr.html.

....We identify a communicative act with its decisiontheoretic (DT) pragmatics, defined as the transformation of the state of knowledge about the decision making situation that the act brings about. We model DT pragmatics using the RMM representation to investigate the utility of the communicative act [6, 5]. 1 The transformation in the agent s decision making situation, as represented by RMM s recursive model structure, may change the expected utilities of alternative actions. It is natural to identify the change of the expected utility brought about by a communicative action as the expected ....

....after executing the act: U(M) U p M (Y ) Gamma U p (X) 1) where U p (X) is the utility of the best action, X , expected before sending the message, and U p M (Y ) is the utility of the best action, Y , expected if the message were to be sent. Further details and discussion is contained in [6, 5]. We now go on to describing our application domain. 3. The Anti Air Defense Domain Our model the anti air domain consists of a number of attacking missiles, in the simplest case two, labelled A and B in Figure 1 (a) and a number of defending units, labelled 1 and 2. For simplicity we assume ....

[Article contains additional citation context not shown here]

P. J. Gmytrasiewicz and E. H. Durfee. Rational interaction in multiagent environments: Communication. Submitted for publication, 1997. Available in postscript from http://wwwcse. uta.edu/¸piotr/piotr.html.

....its decision theoretic (DT) pragmatics, defined as the transformation of the state of knowledge about the decision making situation (i.e. the recursive model structure) the act brings about. We model DT pragmatics using the RMM representation to investigate the utility of the communicative acts [6, 5]. 3 The transformation in the agent s decision making situation, as represented by RMM s recursive model structure, may change the expected utilities of alternative actions. It is natural to identify the change of the expected utility brought about by a communicative action as the expected ....

....and after executing the act: U(M) U p M (Y ) Gamma Up (X) 2) where Up (X) is the utility of the best action, X, expected before sending the message, and U p M (Y ) is the utility of the best action, Y , expected if the message were to be sent. Further details and discussion is contained in [6, 5]. We now apply DT pragmatics to our anti air defense domain. 3.1 Intentional and Modeling Messages Our implementation of communicating autonomous agents is closely related to BDI theories that describe the agent s Beliefs, Desires, and Intentions [2, 16] In a coordinated multi agent ....

[Article contains additional citation context not shown here]

P. J. Gmytrasiewicz and E. H. Durfee. Rational interaction in multiagent environments: Communication. Submitted for publication, 1997. Available in postscript from http://www-cse.uta.edu/¸piotr/piotr.html.

....the rational one in the situation at hand [8] Within our approach in RMM this parameter is formally introduced into the solution method in the section below. 3 The Formalizm of Recursive Modeling In this section we provide a brief overwiev of the Recursive Modeling Method previously presented in [18, 19, 20]. The Recursive Modeling Method consists of a modeling structure that represents the an agent s knowledge in all of its nested levels, and the solution method that traverses the structure equivalent to the original modeling structure to arrive at the rational choice of an agent s action in a ....

....In both cases, if the intentionality and rationality was not used to arrive at these predictions, they can be treated as sub intentional models within the RMM framework. The definition of the recursive model structure and the intentional model are recursive, but, as we argued in more detail in [18, 19, 20], and as suggested by Halpern and Moses in [22] the recursion is bound to end due to practical limitations in attaining infinite knowledge. Intuitively, agents that have interacted for a finite amount of time could possibly have exchanged only a finite number, say , of messages. Therefore, the ....

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Piotr J. Gmytrasiewicz and Edmund H. Durfee. Rational interaction in multiagent environments. Submitted to Artificial Intelligence, 1995.

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Gmytrasiewicz, P. and E. Durfee, (ms), Rational Interactions in Multiagent Environments, University of Texas and University of Michigan.

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