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Rajiv Maheswaran

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16 papers
1 author row

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16

AAMAS Conference 2013 Conference Paper

Modeling Human Adversary Decision Making in Security Games: An Initial Report

  • Thanh H. Nguyen
  • James Pita
  • Rajiv Maheswaran
  • Milind Tambe
  • Amos Azaria
  • Sarit Kraus

Motivated by recent deployments of Stackelberg security games (SSGs), two competing approaches have emerged which either integrate models of human decision making into game-theoretic algorithms or apply robust optimization techniques that avoid adversary modeling. Recently, a robust technique (MATCH) has been shown to significantly outperform the leading modeling-based algorithms (e. g. , Quantal Response (QR)) even in the presence of significant amounts of subject data. As a result, the effectiveness of using human behaviors in solving SSGs remains in question. We study this question in this paper.

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AAMAS Conference 2012 Conference Paper

A Robust Approach to Addressing Human Adversaries in Security Games

  • James Pita
  • Richard John
  • Rajiv Maheswaran
  • Milind Tambe
  • Rong Yang
  • Sarit Kraus

While game-theoretic approaches have been proposed for addressing complex security resource allocation problems, many of the standard game-theoretic assumptions fail to address human adversaries who security forces will likely face. To that end, approaches have been proposed that attempt to incorporate better models of human decision-making in these security settings. We take a new approach where instead of trying to create a model of human decisionmaking, we leverage ideas from robust optimization techniques. In addition, we extend our approach and the previous best performing approach to also address human anchoring biases under limited observation conditions. To evaluate our approach, we perform a comprehensive examination comparing the performance of our new approach against the current leading approaches to addressing human adversaries. Finally, in our experiments we take the first ever analysis of some demographic information and personality measures that may influence decision making in security games.

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AAMAS Conference 2012 Conference Paper

Designing Better Strategies against Human Adversaries in Network Security Games

  • Rong Yang
  • Fei Fang
  • Albert Xin Jiang
  • Karthik Rajagopal
  • Milind Tambe
  • Rajiv Maheswaran

In a Network Security Game (NSG), security agencies must allocate limited resources to protect targets embedded in a network, such as important buildings in a city road network. A recent line of work relaxed the perfect-rationality assumption of human adversary and showed significant advantages of incorporating the bounded rationality adversary models in non-networked security domains. Given that real-world NSG are often extremely complex and hence very difficult for humans to solve, it is critical that we address human bounded rationality when designing defender strategies. To that end, the key contributions of this paper include: (i) comprehensive experiments with human subjects using a web-based game that we designed to simulate NSGs; (ii) new behavioral models of human adversary in NSGs, which we train with the data collected from human experiments; (iii) new algorithms for computing the defender optimal strategy against the new models.

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AAMAS Conference 2012 Conference Paper

Improving Building Energy Efficiency with a Network of Sensing, Learning and Prediction Agents

  • Sunil Mamidi
  • Yu-Han Chang
  • Rajiv Maheswaran

Nearly 20% of total energy consumption in the United States is accounted for in heating, ventilation, and air conditioning (HVAC) systems. Smart sensing and adaptive energy management agents can greatly decrease the energy usage of HVAC systems in many building applications, for example by enabling the operator to shut off HVAC to unoccupied rooms. We implement a multi-modal sensor agent that is non-intrusive and low-cost, combining information such as motion detection, CO2 reading, sound level, ambient light, and door state sensing. We show that in our live testbed at the USC campus, these sensor agents can be used to accurately estimate the number of occupants in each room using machine learning techniques, and that these techniques can also be applied to predict future occupancy by creating agent models of the occupants. These predictions will be used by control agents to enable the HVAC system increase its efficiency by continuously adapting to occupancy forecasts of each room.

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AAMAS Conference 2012 Conference Paper

SAVES: A Sustainable Multiagent Application to Conserve Building Energy Considering Occupants

  • Jun-young Kwak
  • Pradeep Varakantham
  • Rajiv Maheswaran
  • Milind Tambe
  • Farrokh Jazizadeh
  • Geoffrey Kavulya
  • Laura Klein
  • Burcin Becerik-Gerber

This paper describes an innovative multiagent system called SAVES with the goal of conserving energy in commercial buildings. We specifically focus on an application to be deployed in an existing university building that provides several key novelties: (i) jointly performed with the university facility management team, SAVES is based on actual occupant preferences and schedules, actual energy consumption and loss data, real sensors and hand-held devices, etc. ; (ii) it addresses novel scenarios that require negotiations with groups of building occupants to conserve energy; (iii) it focuses on a non-residential building, where human occupants do not have a direct financial incentive in saving energy and thus requires a different mechanism to effectively motivate occupants; and (iv) SAVES uses a novel algorithm for generating optimal MDP policies that explicitly consider multiple criteria optimization (energy and personal comfort) as well as uncertainty over occupant preferences when negotiating energy reduction - this combination of challenges has not been considered in previous MDP algorithms. In a validated simulation testbed, we show that SAVES substantially reduces the overall energy consumption compared to the existing control method while achieving comparable average satisfaction levels for occupants. As a real-world test, we provide results of a trial study where SAVES is shown to lead occupants to conserve energy in real buildings.

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AAMAS Conference 2011 Conference Paper

The Social Ultimatum Game and Adaptive Agents

  • Yu-Han Chang
  • Rajiv Maheswaran

The Ultimatum Game is a key exemplar that shows how human play often deviates from "rational" strategies suggested by game-theoretic analysis. One explanation is that humans cannot put aside the assumption of being in a multi-player multi-round environment that they are accustomed to in the real world. We introduce the Social Ultimatum Game (SUG), where players can choose their partner among a society of agents, and engage in repeated interactions of the Ultimatum Game. We develop mathematical models of human play that include "irrational" concepts such as fairness and adaptation to the expectations of the society. We will display a system where people can play SUG against a mixed system of other humans and autonomous agents based on our mathematical models.

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AAMAS Conference 2008 Conference Paper

Look Where You Can See: Predictability & Criticality Metrics for Coordination in Complex Environments

  • Rajiv Maheswaran
  • Pedro Szekely
  • Marcel Becker
  • Stephen Fitzpatrick
  • Gergely Gati
  • Jing Jin
  • Robert Neches
  • Nader Noori

We address the problem of coordinating the activities of a team of agents in a dynamic, uncertain, nonlinear environment. Bounded rationality, bounded communication, subjectivity and distribution make it extremely challenging to find effective strategies. In these domains it is difficult to accurately predict whether potential policy modifications will lead to an increase in the value of the team reward. Our Predictability and Criticality Metrics (PCM) approach errs on the side of safety, and advocates considering policy modifications that are guaranteed to not harm the current policy, and uses simple metrics to choose from within that set a modification that increases the team reward. In the context of the DARPA Coordinators program, we show how the PCM approach yielded a system that significantly outperformed several competing approaches in an extensive independent evaluation. General Terms Algorithms

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IJCAI Conference 2007 Conference Paper

  • Pradeep Varakantham
  • Rajiv Maheswaran
  • Tapana Gupta
  • Milind Tambe

While POMDPs (partially observable markov decision problems) are a popular computational model with wide-ranging applications, the computational cost for optimal policy generation is prohibitive. Researchers are investigating ever-more efficient algorithms, yet many applications demand such algorithms bound any loss in policy quality when chasing efficiency. To address this challenge, we present two new techniques. The first approximates in the value space to obtain solutions efficiently for a pre-specified error bound. Unlike existing techniques, our technique guarantees the resulting policy will meet this bound. Furthermore, it does not require costly computations to determine the quality loss of the policy. Our second technique prunes large tracts of belief space that are unreachable, allowing faster policy computation without any sacrifice in optimality. The combination of the two techniques, which are complementary to existing optimal policy generation algorithms, provides solutions with tight error bounds efficiently in domains where competing algorithms fail to provide such tight bounds.

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