Chris Kiekintveld Research Interests: artificial intelligence, multi-agent systems, computational game theory, adversarial reasoning, risk analysis, machine learning, trading agents, supply chain management, security, distributed optimization, mechanism design, behavioral game theory, team and coalition formation

I am an associate professor in computer science at the University of Texas at El Paso. My research work in the area of artificial intelligence is driven by fundamental questions about how we can use computational analysis techniques to make good decisions in highly complex environments. I am especially interested in situations with multiple decision-makers, which raises questions about how to predict and react to the behavior of other intelligent agents (including humans) in both cooperative and adversarial settings. In artificial intelligence, this class of problems is often referred to as multi-agent systems. Some examples of multi-agent systems include teams of rescue workers, soldiers, or robots coordinating to achieve a mission, trading agents participating in auctions of financial markets, and security forces trying to prevent attacks on critical infrastructure or computer networks.

My basic research centers around developing novel techniques for strategic reasoning in multi-agent domains, including new algorithms, models, and computing methodologies that exploit the availability of computational resources to scale to ever larger and more complex domains than existing methods. This work contributes to computational game theory, adversarial reasoning, distributed optimization, agent-based simulation, and other related areas. The unifying theme across these areas is predicting how other intelligent agents will act, based on knowledge of their capabilities, goals, historical patterns of behavior, and other factors. One goal of my work is to develop computationally scalable methods for systems with hundreds or thousands of agents and massive strategy spaces. I am also very interested in developing techniques for improving the robustness of solutions to various types of uncertainty, including model uncertainty and bounded rationality.

Working on real-world applications is an important element of my research approach, and often provides great inspiration to address fundamental challenges. I have interests in a variety of application areas, including electronic commerce, infrastructure security, transportation, and energy. Recently I have worked extensively on applications of game theory to infrastructure protection, including a decision support system called IRIS that is currently deployed by the Federal Air Marshals Service (FAMS) and a second system that is in testing for nationwide deployment by the Transportation Security Administration. I have several new projects underway in this area, including work on border security applications. Another area that I have been heavily involved in is automated trading agents for e-Commerce applications. I participated in the Trading Agent Competition, and was a lead developer for the Deep Maize agent, which won the tournament in 2008 and has consistently been among the top few performers in every tournament to date.

Prior to joining UTEP I was a postdoctoral reseach associate at the University of Southern California, where I worked with Milind Tambe and the Teamcore research group. I received my Ph.D. from the University of Michigan, where I was advised by Michael Wellman. During my time at Michigan I was active as a fellow of the STIET program, which provides interdisciplinary training and brings together researchers from diverse disciplines with shared interests in incentive-centered design.

Contact Information
UAI tutorial

An invited tutorial given by Milind Tambe and Chris Kiekintveld at UAI 2011: Game Theory for Security: Lessons learned from deployed applications

Uncertainty in Security Games

A talk given at the International Workshop on Safety, Security, and Efficiency for Critical Infrastructures: An Overview of Recent Progress on Uncertainty in Security Games