Paper Info
Title
Goal-Driven autonomy with case-based reasoning
Abstract
The vast majority of research on AI planning has focused on automated plan recognition, in which a planning agent is provided with a set of inputs that include an initial goal (or set of goals). In this context, the goal is presumed to be static; it never changes, and the agent is not provided with the ability to reason about whether it should change this goal. For some tasks in complex environments, this constraint is problematic; the agent will not be able to respond to opportunities or plan execution failures that would benefit from focusing on a different goal. Goal driven autonomy (GDA) is a reasoning framework that was recently introduced to address this limitation; GDA systems perform anytime reasoning about what goal(s) should be satisfied [4]. Although promising, there are natural roles that case-based reasoning (CBR) can serve in this framework, but no such demonstration exists. In this paper, we describe the GDA framework and describe an algorithm that uses CBR to support it. We also describe an empirical study with a multiagent gaming environment in which this CBR algorithm outperformed a rule-based variant of GDA as well as a non-GDA agent that is limited to dynamic replanning.
Year
DOI
Venue
2010
10.1007/978-3-642-14274-1_18
ICCBR
Keywords
Field
DocType
non-gda agent,gda system,planning agent,case-based reasoning,ai planning,reasoning framework,initial goal,different goal,goal-driven autonomy,cbr algorithm,gda framework,satisfiability,system performance,empirical study,case base reasoning,rule based
Hierarchical task network,Computer science,Autonomy,Artificial intelligence,Plan recognition,Case-based reasoning,Empirical research,Machine learning,Automated planning and scheduling
Conference
Volume
ISSN
ISBN
6176
0302-9743
3-642-14273-7
Citations 
PageRank 
References 
9
0.60
14
Authors
4
Name
Order
Citations
PageRank
Héctor Muñoz-Avila167455.13
Ulit Jaidee2453.55
David W. Aha34103620.93
Elizabeth Carter490.60