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Ed Durfee

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.

7 papers
1 author row

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7

AAMAS Conference 2012 Conference Paper

A Decision-Theoretic Characterization of Organizational Influences

  • Jason Sleight
  • Ed Durfee

Despite a large body of research on integrating organizational concepts into cooperative multiagent systems, a formal understanding of how organizations can influence agents' decisions remains elusive. This paper works toward such an understanding by beginning with a model of agent decision making based on decision-theoretic principles, and then examining the possible routes that organizational influences can take to affect that model. We show that alternative avenues of applying influences correspond to different prior notions of organizational control, and empirically demonstrate the impact that each can have on the quality and overhead of coordinated behavior. To do so, we must define the agents' baseline behavior (without a designed organization), and we present a methodology for initializing agents' models to comprise what amounts to an "uninformed" organization. Finally, we show how the specification of organizational influences in terms of components of a decision-theoretic agent creates opportunities for agents to compare actual events with predictions implied in the models, such that agents can reason about whether to change organizations. We demonstrate that this capability to question and change organizations can be valuable if used judiciously.

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AAMAS Conference 2012 Conference Paper

Planning and Evaluating Multiagent Influences Under Reward Uncertainty

  • Stefan Witwicki
  • Inn-Tung Chen
  • Ed Durfee
  • Satinder Singh

Forming commitments about abstract influences that agents can exert on one another has shown promise in improving the tractability of multiagent coordination under uncertainty. We now extend this approach to domains with meta-level reward-model uncertainty. Intuitively, an agent may actually improve collective performance by forming a weaker commitment that allows more latitude to adapt its policy as it refines its reward model. To account for reward uncertainty as such, we introduce and contrast three new techniques.

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AAMAS Conference 2010 Conference Paper

From Policies to Influences: A Framework For Nonlocal Abstraction In Transition-dependent Dec-POMDP Agents

  • Stefan Witwicki
  • Ed Durfee

Decentralized POMDPs are powerful theoretical models for coordinating agents' decisions in environments with uncertainty, but the generally intractable complexity of optimal joint policy construction presents a significant obstacle in applying DEC-POMDPs to problems where many agents face many policy choices. Here, we argue that when most agent choices are independent of peers' choices, much of this complexity can be avoided: instead of coordinating full policies, agents need only coordinate policy abstractions that explicitly convey the essential interaction influences. To this end, we develop a novel framework for abstracting the influences of a general class of transition-dependent Dec-POMDP agents where the compactness of agents' nonlocal models is a function of the degree to which they interact with their peers (and not the number of peers). In addition to the computational advantages over state-of-the-art policy search method (supported by an initial empirical comparison), our framework has the benefits of agent privacy and flexibility for approximation.

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AAMAS Conference 2010 Conference Paper

Generalized Solution Techniques for Preference-Based Constraint Optimization with CP-nets

  • James Boerkoel
  • Ed Durfee
  • Keith Purrington

Computational agents can assist people by guiding their decisions in ways that achieve their goals while also adhering to constraints on their actions. Because some domains are more naturally modeled by representing preferences and constraints separately, we seek to develop efficient techniques for solving such decoupled constraint optimization problems. This paper describes a parameterized formulation for decoupled constraint optimization problems that subsumes the state-of-the-art algorithm of Boutilier et al, representing a wider family of alternative algorithms. We empirically examine notable members of this family to highlight the spaces of decoupled constraint optimization problems for which each excels, highlight fundamental relationships between different algorithmic variations, and use these insights to create and evaluate novel hybrids of these algorithms that a cognitive assistant agent can use to flexibly trade off solution quality with computational time.

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