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Emma Bowring

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11

AAAI Conference 2014 Conference Paper

Give a Hard Problem to a Diverse Team: Exploring Large Action Spaces

  • Leandro Soriano Marcolino
  • Haifeng Xu
  • Albert Xin Jiang
  • Milind Tambe
  • Emma Bowring

Recent work has shown that diverse teams can outperform a uniform team made of copies of the best agent. However, there are fundamental questions that were not asked before. When should we use diverse or uniform teams? How does the performance change as the action space or the teams get larger? Hence, we present a new model of diversity for teams, that is more general than previous models. We prove that the performance of a diverse team improves as the size of the action space gets larger. Concerning the size of the diverse team, we show that the performance converges exponentially fast to the optimal one as we increase the number of agents. We present synthetic experiments that allow us to gain further insights: even though a diverse team outperforms a uniform team when the size of the action space increases, the uniform team will eventually again play better than the diverse team for a large enough action space. We verify our predictions in a system of Go playing agents, where we show a diverse team that improves in performance as the board size increases, and eventually overcomes a uniform team.

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JAAMAS Journal 2013 Journal Article

Empirical evaluation of computational fear contagion models in crowd dispersions

  • Jason Tsai
  • Emma Bowring
  • Milind Tambe

Abstract In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions being influenced by surrounding people’s emotions. While the overall effect is agreed upon, the underlying mechanism of the spread of emotions has seen little quantification and application to computational agents despite extensive evidence of its impacts in everyday life. In this paper, we examine computational models of emotional contagion by implementing two models (Bosse et al. , European council on modeling and simulation, pp. 212–218, 2009 ) and Durupinar, From audiences to mobs: Crowd simulation with psychological factors, PhD dissertation, Bilkent University, 2010 ) that draw from two separate lines of contagion research: thermodynamics-based and epidemiological-based. We first perform sensitivity tests on each model in an evacuation simulation, ESCAPES, showing both models to be reasonably robust to parameter variations with certain exceptions. We then compare their ability to reproduce a real crowd panic scene in simulation, showing that the thermodynamics-style model (Bosse et al. , European council on modeling and simulation, pp. 212–218, 2009 ) produces superior results due to the ill-suited contagion mechanism at the core of epidemiological models. We also identify that a graduated effect of fear and proximity-based contagion effects are key to producing the superior results. We then reproduce the methodology on a second video, showing that the same results hold, implying generality of the conclusions reached in the first scene.

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AAMAS Conference 2012 Conference Paper

Emotional Contagion with Virtual Characters

  • Jason Tsai
  • Emma Bowring
  • Stacy Marsella
  • Milind Tambe

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions mimicking surrounding people’s emotions [8]. While it has been observed in humanhuman interactions, no known studies have examined its existence in agent-human interactions. As virtual characters make their way into high-risk, high-impact applications such as psychotherapy and military training with increasing frequency, the emotional impact of the agents’ expressions must be accurately understood to avoid undesirable repercussions.

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AAMAS Conference 2011 Conference Paper

ESCAPES - Evacuation Simulation with Children, Authorities, Parents, Emotions, and Social comparison

  • Jason Tsai
  • Natalie Fridman
  • Emma Bowring
  • Matthew Brown
  • Shira Epstein
  • Gal A. Kaminka
  • Stacy Marsella
  • Andrew Ogden

In creating an evacuation simulation for training and planning, realistic agents that reproduce known phenomenon are required. Evacuation simulation in the airport domain requires additional features beyond most simulations, including the unique behaviors of first-time visitors who have incomplete knowledge of the area and families that do not necessarily adhere to often-assumed pedestrian behaviors. Evacuation simulations not customized for the airport domain do not incorporate the factors important to it, leading to inaccuracies when applied to it. In this paper, we describe ESCAPES, a multiagent evacuation simulation tool that incorporates four key features: (i) different agent types; (ii) emotional interactions; (iii) informational interactions; (iv) behavioral interactions. Our simulator reproduces phenomena observed in existing studies on evacuation scenarios and the features we incorporate substantially impact escape time. We use ESCAPES to model the International Terminal at Los Angeles International Airport (LAX) and receive high praise from security officials.

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AAMAS Conference 2011 Conference Paper

Quality Guarantees for Region Optimal DCOP Algorithms

  • Meritxell Vinyals
  • Eric Shieh
  • Jesus Cerquides
  • Juan Antonio Rodriguez-Aguilar
  • Zhengyu Yin
  • Milind Tambe
  • Emma Bowring

k - and t -optimality algorithms provide solutions to DCOPs that are optimal in regions characterized by its size and distance respectively. Moreover, they provide quality guarantees on their solutions. Here we generalise the k - and t -optimal framework to introduce C -optimality, a flexible framework that provides reward-independent quality guarantees for optima in regions characterised by any arbitrary criterion. Therefore, C -optimality allows us to explore the space of criteria (beyond size and distance) looking for those that lead to better solution qualities. We benefit from this larger space of criteria to propose a new criterion, the socalled size-bounded-distance criterion, which outperforms k - and t -optimality.

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AAMAS Conference 2009 Conference Paper

Sensitivity Analysis for Distributed Optimization with Resource Constraints

  • Emma Bowring
  • Zhengyu Yin
  • Rob Zinkov
  • Milind Tambe

Previous work in multiagent coordination has addressed the challenge of planning in domains where agents must optimize a global goal, while satisfying local resource constraints. However, the imposition of resource constraints naturally raises the question of whether the agents could significantly improve their team performance if a few more resources were made available. Sensitivity analysis aims to answer that question. This paper focuses on sensitivity analysis in the context of the distributed coordination framework, Multiply-Constrained DCOP (MC-DCOP). There are three main challenges in performing sensitivity analysis: (i) to perform it in a distributed fashion, (ii) to avoid re-solving an NP-hard MC-DCOP optimization from scratch, and (iii) to avoid considering unproductive uses for extra resources. To meet these challenges, this paper presents three types of locally optimal algorithms: link analysis, local reoptimization and local constraint propagation. These algorithms are distributed and avoid redundant computation by ascertaining just the effects of local perturbations on the original problem. Deploying our algorithms on a large number of MC-DCOP problems revealed several results. While our cheapest algorithm successfully identified quality improvements for a few problems, our more complex techniques were necessary to identify the best uses for additional resources. Furthermore, we identified two heuristics that can help identify a priori which agents might benefit most from additional resources: density rank, which works well when nodes received identical resources and remaining resource rank, which works well when nodes received resources based on the number of neighbors they had.

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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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