Author name cluster

Xia Qu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

NeurIPS Conference 2015 Conference Paper

Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability

Xia Qu
Prashant Doshi

This paper provides the first formalization of self-interested planning in multiagent settings using expectation-maximization (EM). Our formalization in the context of infinite-horizon and finitely-nested interactive POMDPs (I-POMDP) is distinct from EM formulations for POMDPs and cooperative multiagent planning frameworks. We exploit the graphical model structure specific to I-POMDPs, and present a new approach based on block-coordinate descent for further speed up. Forward filtering-backward sampling -- a combination of exact filtering with sampling -- is explored to exploit problem structure.

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AAMAS Conference 2012 Conference Paper

Modeling Deep Strategic Reasoning by Humans in Competitive Games

Xia Qu
Prashant Doshi
Adam Goodie

The prior literature on strategic reasoning by humans of the sort, what do you think that I think that you think, is that humans generally do not reason beyond a single level. However, recent evidence suggests that if the games are made competitive and therefore representationally simpler, humans generally exhibited behavior that was more consistent with deeper levels of recursive reasoning. We seek to computationally model behavioral data that is consistent with deep recursive reasoning in competitive games. We use generative, process models built from agent frameworks that simulate the observed data well and also exhibit psychological intuition.

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AAMAS Conference 2010 Conference Paper

Modeling Recursive Reasoning by Humans Using Empirically Informed Interactive POMDPs

Prashant Doshi
Xia Qu
Adam Goodie
Diana Young

Recursive reasoning of the form {\em what do I think that you think that I think} (and so on) arises often while acting rationally in multiagent settings. Several multiagent decision-making frameworks such as RMM, I-POMDP and the theory of mind model recursive reasoning as integral to an agent's rational choice. Real-world application settings for multiagent decision making are often mixed involving humans and human-controlled agents. In two large experiments, we studied the level of recursive reasoning generally displayed by humans while playing sequential general-sum and fixed-sum, two-player games. Our results show that subjects experiencing a general-sum strategic game display first or second level of recursive thinking with the first level being more prominent. However, if the game is made simpler and more competitive with fixed-sum payoffs, subjects predominantly attributed first-level recursive thinking to opponents thereby acting using second level of reasoning. Subsequently, we model the behavioral data obtained from the studies using the I-POMDP framework, appropriately augmented using well-known human judgment and decision models. Accuracy of the predictions by our models suggest that these could be viable ways for computationally modeling strategic behavioral data.

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