Using Expressive and Flexible Action Representations to Reason about Capabilties for Intelligent Agent Cooperation
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The aim of this thesis is to adress the problem of capability brokering. A capability-brokering agent recieves capability advertisements from problem-solving agents and problem descriptions from problem-holding agents. The amin task for the broker is to find problem-solving agents that have the capabilities to address problems described to the broker by a problem-holding agent. Capability brokering poses two problems: for advertisements, and matching problems and capabilities, to find capable problem-solvers. For the representation part of the problem, there have been a number of representations in AI that address similar issues. We review various logical representations, action representations, and representations for models of problem solving and conclude that, while all of these areas have some positive features for the representation of capabilities, they also all have serious drawbacks. We describe a new capability description language, CDL, which shares the positive features of previous languages while avoiding their drawbacks. CDL is a decoupled action representation into which arbitrary state representations can be plugged, resulting in the expressiveness and flexibility needed for capability brokering. Reasoning over capability descriptions takes place on two levels. The outer level deals with agent communication and we have devloped the Knowledge Query and Manipulation Language (KQML) here. At the inner level the main task is to decide whether a capability description subsumes a problem description. In CDL thee subsumtion relation for achievable objectives is defined in terms of the logical entailment relation betwenn sentences in the state language used within CDL. The definition of subsumption for performable tasks in turn is based on this definition for achievable objectives. We describe algoritms in this thesis which have all been implemented and incorporated into he Java Agent Template where they proved sufficient to operationalise anumber of example scenarios. The two most important featues of CDL are its expressiveness and its flexibility. By expressiveness we mean the ability to express more than is possible in other representations. By flexibility we mean the possibility to delay decisions regarding the compromises that have to be made to knowledge representation time. The scenarions we ahve implemted illustrate the importance of the features and we have shown in this thesis that CDL indeed possess thease features. Thus, CDL is an expressive and flexible capability description language that can be used to address the problem of capability brokering.