Piotr J. Gmytrasiewicz

Decision-theoretic cooperative sensor planning

This paper describes a decision-theoretic approach to cooperative sensor planning between multiple autonomous vehicles executing a military mission. For this autonomous vehicle application, intelligent cooperative reasoning must be used to select optimal vehicle viewing locations and select optimal camera pan and tilt angles throughout the mission. Decisions are made in such a way as to maximize the value of information gained by the sensors while maintaining vehicle stealth. Because the mission involves multiple vehicles, cooperation can be used to balance the work load and to increase information gain. This paper presents the theoretical foundations of our cooperative sensor planning research and describes the application of these techniques to ARPAs Unmanned Ground Vehicle program.

Fault tree based diagnostics using fuzzy logic

Fuzzy set theory is investigated as a tool for the diagnostics of systems described by means of a fault tree. The objective is to diagnose component failures from the observation of fuzzy symptoms using the information contained in a fault tree. A two-step procedure is used to solve the problem. In this first step, causal reasoning is used to diagnose failure modes, consisting of minimal cut-sets of basic events, from the observation of triggered gates treated as symptoms. In the second step, the authors identify the particular components which have failed based on the diagnosed failure modes. To perform this second step, the solution of a fuzzy relational equation a= V-product (S/sup T/ alpha x) connecting failure mode a to basic events x is derived. With this method, the diagnostics equations can be symmetrically generated and solved in terms of the trees basic events. The systematic nature with which a diagnosis can be generated from a fault tree lends this method to potential application of object-based programming techniques. >

Toward a theory of honesty and trust among communicating autonomous agents

This article outlines, through a number of examples, a method that can be used by autonomous agents to decide among potential messages to send to other agents, without having to assume that a message must be truthful and that it must be believed by the hearer. The main idea is that communicative behavior of autonomous agents is guided by the principle of economic rationality, whereby agents transmit messages to increase the effectiveness of interaction measured by their expected utilities. We are using a recursive, decision-theoretic formalism that allows agents to model each other and to infer the impact of a message on its recipient. The recursion can be continued into deeper levels, and agents can model the recipient modeling the sender in an effort to assess the truthfulness of the received message. We show how our method often allows the agents to decide to communicate in spite of the possibility that the messages will not be believed. In certain situations, on the other hand, our method shows that the possibility of the hearer not believing what it hears makes communication useless. Our method thus provides the rudiments of a theory of how honesty and trust could emerge through rational, selfish behavior.

Combining decision theory and hierarchical planning for a time-dependent robotic application

The authors present the Rational Reasoning System (RRS), which integrates hierarchical planning with decision theory in order to make rational decisions in time-dependent situations. RRS controls a robot in a simulated hazardous environment and allows it to address time-dependent emergencies of various urgencies. RRS uses decision theory to constrain and focus the hierarchical construction of plans, introducing computationally practical meta-reasoning methods that factor costs of planning into an overall optimal reasoning and acting behavior. The authors illustrate RRSs capabilities in a simulated nuclear power plant. The authors propose that decision theory should be a basis for any truly autonomous rational robotic system.<<ETX>>

Decision-theoretic recursive modeling and the coordinated attack problem

In our decision-theoretic recursive modeling approach, interacting autonomous agents view their own decision making as an attempt to solve a multiperson game they play with other agents. To predict the actions of others, agents model the decision making of other agents (solving their own games), which in turn is based on their models of other agents, and so on. Considering the changes in the recursive hierarchy of models due to communication, we can compute corresponding changes to the utilities the agents can expect in their interactions. The utility of a message can be defined in terms of this change, and agents can choose to communicate only messages of the highest utilities. In this paper, we illustrate the power of our methodology by applying it to the coordinated attack problem, discussed by others using different methodology. We show how autonomous agents using our method are able to coordinate their attacks and defeat the enemy. This result contrasts with results of other work that has used a logic-based approach to decision making about actions.