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Jonathan Pearce

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
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7

AAMAS Conference 2008 Conference Paper

On K-Optimal Distributed Constraint Optimization Algorithms: New Bounds and Algorithms

  • Emma Bowring
  • Jonathan Pearce
  • Christopher Portway
  • Manish Jain
  • Milind Tambe

Distributed constraint optimization (DCOP) is a promising approach to coordination, scheduling and task allocation in multi agent networks. In large-scale or low-bandwidth networks, finding the global optimum is often impractical. K-optimality is a promising new approach: for the first time it provides us a set of locally optimal algorithms with quality guarantees as a fraction of global optimum. Unfortunately, previous work in k-optimality did not address domains where we may have prior knowledge of reward structure; and it failed to provide quality guarantees or algorithms for domains with hard constraints (such as agents’ local resource constraints). This paper addresses these shortcomings with three key contributions. It provides: (i) improved lower-bounds on k-optima quality incorporating available prior knowledge of reward structure; (ii) lower bounds on k-optima quality for problems with hard constraints; and (iii) k-optimal algorithms for solving DCOPs with hard constraints and detailed experimental results on large-scale networks.

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AAMAS Conference 2008 Conference Paper

Playing Games for Security: An Efficient Exact Algorithm for Solving Bayesian Stackelberg Games

  • Praveen Paruchuri
  • Jonathan Pearce
  • Janusz Marecki
  • Milind Tambe
  • Fernando Ordonez
  • Sarit Kraus

In a class of games known as Stackelberg games, one agent (the leader) must commit to a strategy that can be observed by the other agent (the follower or adversary) before the adversary chooses its own strategy. We consider Bayesian Stackelberg games, in which the leader is uncertain about the types of adversary it may face. Such games are important in security domains, where, for example, a security agent (leader) must commit to a strategy of patrolling certain areas, and a robber (follower) has a chance to observe this strategy over time before choosing its own strategy of where to attack. This paper presents an efficient exact algorithm for finding the optimal strategy for the leader to commit to in these games. This algorithm, DOBSS, is based on a novel and compact mixed-integer linear programming formulation. Compared to the most efficient algorithm known previously for this problem, DOBSS is not only faster, but also leads to higher quality solutions, and does not suffer from problems of infeasibility that were faced by this previous algorithm. Note that DOBSS is at the heart of the ARMOR system that is currently being tested for security scheduling at the Los Angeles International Airport.

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