Decision-theoretic Clustering of Strategies

2 years 11 months ago
Decision-theoretic Clustering of Strategies
Clustering agents by their behaviour can be crucial for building effective agent models. Traditional clustering typically aims to group entities together based on a distance metric, where a desirable clustering is one where the entities in a cluster are spatially close together. Instead, one may desire to cluster based on actionability, or the capacity for the clusters to suggest how an agent should respond to maximize their utility with respect to the entities. Segmentation problems examine this decision-theoretic clustering task. Although finding optimal solutions to these problems is computationally hard, greedy-based approximation algorithms exist. However, in settings where the agent has a combinatorially large number of candidate responses whose utilities must be considered, these algorithms are often intractable. In this work, we show that in many cases the utility function can be factored to allow for an efficient greedy algorithm even when there are exponentially large resp...
Nolan Bard, Deon Nicholas, Csaba Szepesvári
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where ATAL
Authors Nolan Bard, Deon Nicholas, Csaba Szepesvári, Michael H. Bowling
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