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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,
Address: Hakodate, Japan May 2006.
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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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Cognitive Assistant that Learns and Organizes
As part of DARPA’s Perceptive Agent that Learns (PAL) program, SRI and team members are working on developing a next-generation "Cognitive Agent that Learns and Organizes" (CALO).
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