TitleCase-based goal-driven coordination of multiple learning agents
Publication TypeConference Paper
Year of Publication2013
AuthorsJaidee, U, Muñoz-Avila, H, Aha, DW
Conference NameInternational Conference on Case-Based Reasoning
Conference LocationSaratoga Springs, NY
Keywordscase-based reasoning, Goal reasoning, goal-driven autonomy, multiple agents

Although several recent studies have been published on goal
reasoning (i.e., the study of agents that can self-select their goals), none have
focused on the task of learning and acting on large state and action spaces. We
introduce GDA-C, a case-based goal reasoning algorithm that divides the state
and action space among cooperating learning agents. Cooperation between
agents emerges because (1) they share a common reward function and (2)
GDA-C formulates the goal that each agent needs to achieve. We claim that its
case-based approach for goal formulation is critical to the agents’ performance.
To test this claim we conducted an empirical study using the Wargus RTS
environment, where we found that GDA-C outperforms its non-GDA ablation.

Refereed DesignationRefereed
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Goal reasoning
goal-driven autonomy
case-based reasoning
multiple agents