We are interested in the problem faced byanagent with limited computational capabilities, embedded in a complex environment with other agents and processes not under its control. Careful management of computational resources is important for complex problem-solving tasks in which the time spent in decision making affects the quality of the responses generated by a system.

This paper describes a real-time decisionmaking model that combines the expressiveness and flexibility of knowledge-based systems with the real-time advantages of anytime algorithms. Anytime algorithms offer

Autonomous agents that respond intelligently in dynamic, complex environments need to be both reactive and deliberative. Reactive systems have traditionally fared better than deliberative planers in such environments, but are often hard to code and inflexible. To fill in some of these gaps, we propose a hybrid system that exploits the strengths of both reactive and deliberative systems. We demonstrate how our system controls a simulated household robot and compare our system to a purely reactive one in this domain. We also look at a number of relevant issues in anytime planning. This work was supported in part by Fujitsu Laboratories, Ltd. and the Avionics Laboratory, Wright Research and Development Center, Aeronautical Systems Division (AFSC), U.S. Air Force, Wright-Patterson AFB, OH 45433-6543 under Contract F33615-90-C-1465, ARPA Order No. 7597. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official po...

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: ...

Much planning research assumes that the goals for which one plans are known in advance. That is not true of trauma management, which involves both a search for relevant goals and reasoning about how to achieve them. TraumAID is a consultation system for the diagnosis and treatment of multiple trauma. It has been under development jointly at the University of Pennsylvania and the Medical College of Pennsylvania for the past eight years. TraumAID integrates diagnostic reasoning, planning and action. Its reasoner identifies diagnostic and therapeutic goals appropriate to the physician 's knowledge of the patient's state, while its planner advises on beneficial actions to next perform. The physician 's lack of complete knowledge of the situation and the time limitations of emergency medicine constrain the ability of any planner to identify what would be the best thing to do. Nevertheless, TraumAID's Progressive Horizon Planner has been designed to create a plan for patient care that is in...

In this paper we present several domain-independent search optimizations and heuristics that have been developed in a totally-ordered nonlinear planner in prodigy. We also describe the extension of the system into a full hierarchical planner with the ability to search among the different levels of abstraction. We analyze and illustrate the performance of the system with its different search capabilities in a few domains.

We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain learning. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently general to facilitate learning in new domains. The results demonstrate transfer from one domain to another can be achieved by deliberately overgeneralizing rules in one domain and biasing the learning algorithm to create new rules that specialize these overgeneralizations in other domains.

Most planners constructed up to now are qualitative: they deal with uncertainty by considering all possible outcomes of each plan, without quantifying their relative likelihood. They then choose a plan that deals with the worstcase scenario. However, it is clearly infeasible to plan for every possible contingency. Even beyond the purely computational considerations, planning for highly unlikely worst-case scenarios can force the agent to choose an overly cautious plan with low utility. One common way to avoid this problem is to make assumptions about the behavior of the world, i.e., assume that certain contingencies are impossible. In this paper, we analyze the paradigm of qualitative planning under assumptions, using decision-theoretic tools. We present conditions that guarantee the existence of optimal assumptions (ones inducing the agent to choose the plan with maximum expected utility). Finally, we sketch how assumptions can be constructed for a certain restricted class of plannin...

This paper propos a reversible architecture to handle not ofily syntactic and semantic information but also pragmatic information. Existing architectures cannot represent pragmatic informatioh explicitly, and lack reasoning capability;given insufficient informa- tion. I argue that 'the techniques of plan rep- resentation and approximate reasoning are, in the enhanced argumentation system proposed here, effective for solving these prob- lems.

of the Dissertation Incremental Search Methods for Real-Time Decision Making by Joseph Carl Pemberton Doctor of Philosophy in Computer Science University of California, Los Angeles, 1995 Professor Richard Korf, Chair Many real-world problems, such as air-traffic control and factory scheduling, require that a sequence of decisions be made in real time, without complete information. Since there is typically not sufficient time for traditional methods to find a complete solution before committing to a decision, we propose an incremental search method for making real-time decisions. Our approach is to separate the real-time decision task into three sub-problems: where to spend limited computational resources?, when to stop computing?, and how to make decisions given incomplete information? By interleaving computation with execution, we can use the execution time to improve the solution quality. We present the last incremental decision problem as a simplification of the general increme...