Authors:
Paul Varkey
and
Piotr Gmytrasiewicz
Affiliation:
University of Illinois at Chicago, United States
Keyword(s):
Bilateral bargaining, Decision theory, Interactive epistemology, Bounded rationality, Memoization.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Autonomous Systems
;
Distributed and Mobile Software Systems
;
Economic Agent Models
;
Enterprise Information Systems
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Planning and Scheduling
;
Simulation and Modeling
;
Software Engineering
;
Symbolic Systems
;
Uncertainty in AI
Abstract:
In this paper, we study the problem of bilateral bargaining under uncertainty. The problem is cast in an interactive decision-theoretic framework, in which the seller and the buyer agents are equipped with the ability to represent and reason with (probabilistic) beliefs about strategically relevant parameters, the other agent’s beliefs, the other agent’s beliefs about the current agent’s beliefs, and so on up to finite levels. The inescapable intractability of solving such models is characterized. We present a realization of the paradigm of (resource) bounded rationality by achieving a trade-off between optimality and efficiency as a function of the discretization resolution of the infinite action space. Memoization is used to further mitigate complexity and is realized here through disk-based caching. In addition, the inevitability of model extinction that arises in such settings is dealt with by indicating an intuitive realization of the absolute continuity condition based on maint
aining an ensemble model, for e.g. a random model, that accounts for all actions not already accounted for by other models. Our results clearly demonstrate an operationalizable scheme for devising computationally efficient anytime algorithms on interactive decision-theoretic foundations for modeling (higher-order) epistemic dynamics and sequential decision making in multi agent domains with uncertainty.
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