Publication Details

Continuous Refinement of Resource Estimates

by Morley, D. N. and Myers, K. L. and Yorke-Smith, N.

in Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS’06) pp. 858-865,

Abstract

The challenge we address is to reason about projected resource usage within a hierarchical task execution framework in order to improve agent effectiveness. Specifically, we seek to define and maintain maximally informative guaranteed bounds on projected resource requirements, in order to enable an agent to take full advantage of available resources while avoiding problems of resource conflict. Our approach is grounded in well-understood techniques for resource projection over possible paths through the plan space of an agent, but introduces three technical innovations. The first is the use of multi-fidelity models of projected resource requirements that provide increasingly more accurate projections as additional information becomes available. The second is execution-time re- finement of initial bounds through pruning possible execution paths and variable domains based on the current world and execution state. The third is exploitation of additional semantic information about tasks that enables improved bounds on resource consumption. In contrast to earlier work in this area, we consider an expressive procedure language that includes complex control constructs and parameterized tasks. The approach has been implemented in the SPARK agent system and is being used to improve the performance of an operational intelligent assistant application.

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Associated Projects

Cognitive Assistant that Learns and Organizes As part of DARPA’s Personalized Assistant that Learns (PAL) program, SRI and team members are working on developing a next-generation "Cognitive Agent that Learns and Organizes" (CALO).

AIC Personnel

	Name	Title	E-mail
	Morley, David N	Alumnus
	Myers, Karen L	Program Director & Principal Scientist
	Yorke-Smith, Neil	Computer Scientist