Goals, as typically conceived in AI planning, provide an insufficient basis for choice of action, and hence are deficient as the sole expression of an agent's objectives. Decision-theoretic utilities offer a more adequate basis, yet lack many of the computational advantages of goals. We provide a preferential semantics for goals that grounds them in decision theory and preserves the validity of some, but not all, common goal operations performed in planning. This semantic account provides a criterion for verifying the design of goal-based planning strategies, thus providing a new framework for knowledge-level analysis of planning systems. Planning to achieve goals In the predominant AI planning paradigm, planners construct plans designed to produce states satisfying particular conditions called goals. Each goal represents a partition of possible states of the world into those satisfying and those not satisfying the goal. Though planners use goals to guide their reasoning, the crude b...

Specification of objectives constitutes a central issue in knowledge representation for planning. Decision-theoretic approaches require that representations of objectives possess a firm semantics in terms of utility functions, yet provide the flexible compositionality needed for practical preference modeling for planning systems. Modularity, or separability in specification, is the key representational feature enabling this flexibility. In the context of utility specification, modularity corresponds exactly to well-known independence concepts from multiattribute utility theory, and leads directly to approaches for composing separate preference specifications. Ultimately, we seek to use this utilitytheoretic account to justify and improve existing mechanisms for specification of preference information, and to develop new representations exhibiting tractable specification and flexible composition of preference criteria. 1 REPRESENTING UTILITY FOR PLANNING As generally conceived, the AI...

This dissertation describes the formal foundations and implementation of a commonsense, mixed-initiative plan reasoning system. By "plan reasoning" I mean the complete range of cognitive tasks that people perform with plans including, for example, plan construction (planning), plan recognition, plan evaluation and comparison, and plan repair (replanning), among other things. "Mixed-initiative" means that several participants can each make contributions to the plan under development through some form of communication. "Commonsense" means that the system represents plans and their constituents at a level that is "natural" to us in the sense that they can be described and discussed in language. In addition, the reasoning that the system performs includes those conclusions that we would take to be sanctioned by common sense, including especially those conclusions that are defeasible given additional knowledge or time spent reasoning. The main theses of this dissertation are the following: ...

The development of Logic Programming and Defeasible Argumentation lead to Defeasible Logic Programming.

Abstract. Negotiation can be conceived as the exchange of messages among self-interested agents in order to settle on an agreement over a given issue. They decide which messages to send according to their preferences and their evolving beliefs. Agents able to handle this dynamics of messages and beliefs can be represented by means of Defeasible Logic Programming augmented with utility functions. This approach to argumentation has the advantage of providing a useful platform for the representation of beliefs and the generation of messages. The interactive nature of negotiations requires an updating mechanism to be applied over the knowledge bases of the agents. The features of this mechanism are described by a protocol of a negotiation. Although there are many possible protocols, we concentrate on one that ensures the existence of an agreement in negotiations. The formalism of DeLP provides a very natural approach to the characterization of such a protocol. 1