Report Number: CS-TR-94-1503
Institution: Stanford University, Department of Computer Science
Title: Deciding whether to plan to react
Author: Dabija, Vlad G.
Date: February 1994
Abstract: Intelligent agents that operate in real-world real-time environments have limited resources. An agent must take these limitations into account when deciding which of two control modes - planning versus reaction - should control its behavior in a given situation. The main goal of this thesis is to develop a framework that allows a resource-bounded agent to decide at planning time which control mode to adopt for anticipated possible run-time contingencies. Using our framework, the agent: (a) analyzes a complete (conditional) plan for achieving a particular goal; (b) decides which of the anticipated contingencies require and allow for preparation of reactive responses at planning time; and (c) enhances the plan with prepared reactions for critical contingencies, while maintaining the size of the plan, the planning and response times, and the use of all other critical resources of the agent within task-specific limits. For a given contingency, the decision to plan or react is based on the characteristics of the contingency, the associated reactive response, and the situation itself. Contingencies that may occur in the same situation compete for reactive response preparation because of the agent's limited resources. The thesis also proposes a knowledge representation formalism to facilitate the acquisition and maintenance of knowledge involved in this decision process. We also show how the proposed framework can be adapted for the problem of deciding, for a given contingency, whether to prepare a special branch in the conditional plan under development or to leave the contingency for opportunistic treatment at execution time. We make a theoretical analysis of the properties of our framework and then demonstrate them experimentally. We also show experimentally that this framework can simulate several different styles of human reactive behaviors described in the literature and, therefore, can be useful as a basis for describing and contrasting such behaviors. Finally we demonstrate that the framework can be applied in a challenging real domain. That is: (a) the knowledge and data needed for the decision making within our framework exist and can be acquired from experts, and (b) the behavior of an agent that uses our framework improves according to response time, reliability and resource utilization criteria.
http://i.stanford.edu/pub/cstr/reports/cs/tr/94/1503/CS-TR-94-1503.pdf