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Jason Tsai

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.

9 papers
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9

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 2013 Conference Paper

Security Games with Contagion: Handling Asymmetric Information

  • Jason Tsai
  • Yundi Qian
  • Yevgeniy Vorobeychik
  • Christopher Kiekintveld
  • Milind Tambe

Counterinsurgency, which is the effort to mitigate support for an opposing organization, is one such domain that has been studied recently and past work has modeled the problem as an influence blocking maximization that features an influencer and a mitigator. While past work has introduced scalable heuristic techniques for generating effective strategies using a double oracle algorithm, it has not addressed the issue of uncertainty and asymmetric information, which is the topic of this paper.

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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 2012 Conference Paper

Security Games and Contagion

  • Jason Tsai

Many real-world situations involve attempts to spread influence through a social network. For example, viral marketing is when a marketer selects a few people to receive some initial advertisement in the hopes that these 'seeds' will spread the news. Even peacekeeping operations in one area have been shown to have a contagious effect on the neighboring vicinity. Each of these domains also features multiple parties seeking to maximize or mitigate a contagious effect by spreading its own influence among a select few seeds, naturally yielding an adversarial resource allocation problem. As past researchers of security resource allocation have done, I propose using game theory to develop such policies and model the interconnected network of people as a graph. Unlike this past work in security games, however, actions in these domains possess a probabilistic, non-local impact that makes even payoff determination an NP-Hard problem. My thesis proposes novel techniques for solving this type of game for real-world problem sizes by building upon the latest research in security games and influence blocking maximization. I have also advanced the understanding of contagion phenomena by developing empirical evaluation methods for computational contagion models. Finally, my thesis formalizes an entirely new class of security games with wide-ranging applications from marketing to peacekeeping.

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AAAI Conference 2012 Conference Paper

Security Games for Controlling Contagion

  • Jason Tsai
  • Thanh Nguyen
  • Milind Tambe

Many strategic actions carry a ‘contagious’ component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address this new class of security games, we combine recent research in influence blocking maximization with a double oracle approach and create novel heuristic oracles to generate mixed strategies for a real-world leadership network from Afghanistan, synthetic leadership networks, and a real social network. We find that leadership networks that exhibit highly interconnected clusters can be solved equally well by our heuristic methods, but our more sophisticated heuristics outperform simpler ones in less interconnected social networks.

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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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AAAI Conference 2010 Conference Paper

Urban Security: Game-Theoretic Resource Allocation in Networked Domains

  • Jason Tsai
  • Zhengyu Yin
  • Jun-young Kwak
  • David Kempe
  • Christopher Kiekintveld
  • Milind Tambe

Law enforcement agencies frequently must allocate limited resources to protect targets embedded in a network, such as important buildings in a city road network. Since intelligent attackers may observe and exploit patterns in the allocation, it is crucial that the allocations be randomized. We cast this problem as an attacker-defender Stackelberg game: the defender’s goal is to obtain an optimal mixed strategy for allocating resources. The defender’s strategy space is exponential in the number of resources, and the attacker’s exponential in the network size. Existing algorithms are therefore useless for all but the smallest networks. We present a solution approach based on two key ideas: (i) A polynomial-sized game model obtained via an approximation of the strategy space, solved efficiently using a linear program; (ii) Two efficient techniques that map solutions from the approximate game to the original, with proofs of correctness under certain assumptions. We present in-depth experimental results, including an evaluation on part of the Mumbai road network.

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AAMAS Conference 2009 Conference Paper

Computing Optimal Randomized Resource Allocations for Massive Security Games

  • Christopher Kiekintveld
  • Manish Jain
  • Jason Tsai
  • James Pita
  • Fernando Ordóñez
  • Milind Tambe

Predictable allocations of security resources such as police officers, canine units, or checkpoints are vulnerable to exploitation by attackers. Recent work has applied game-theoretic methods to find optimal randomized security policies, including a fielded application at the Los Angeles International Airport (LAX). This approach has promising applications in many similar domains, including police patrolling for subway and bus systems, randomized baggage screening, and scheduling for the Federal Air Marshal Service (FAMS) on commercial flights. However, the existing methods scale poorly when the security policy requires coordination of many resources, which is central to many of these potential applications. We develop new models and algorithms that scale to much more complex instances of security games. The key idea is to use a compact model of security games, which allows exponential improvements in both memory and runtime relative to the best known algorithms for solving general Stackelberg games. We develop even faster algorithms for security games under payoff restrictions that are natural in many security domains. Finally, introduce additional realistic scheduling constraints while retaining comparable performance improvements. The empirical evaluation comprises both random data and realistic instances of the FAMS and LAX problems. Our new methods scale to problems several orders of magnitude larger than the fastest known algorithm.

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