146 research outputs found
Human-Agent Decision-making: Combining Theory and Practice
Extensive work has been conducted both in game theory and logic to model
strategic interaction. An important question is whether we can use these
theories to design agents for interacting with people? On the one hand, they
provide a formal design specification for agent strategies. On the other hand,
people do not necessarily adhere to playing in accordance with these
strategies, and their behavior is affected by a multitude of social and
psychological factors. In this paper we will consider the question of whether
strategies implied by theories of strategic behavior can be used by automated
agents that interact proficiently with people. We will focus on automated
agents that we built that need to interact with people in two negotiation
settings: bargaining and deliberation. For bargaining we will study game-theory
based equilibrium agents and for argumentation we will discuss logic-based
argumentation theory. We will also consider security games and persuasion games
and will discuss the benefits of using equilibrium based agents.Comment: In Proceedings TARK 2015, arXiv:1606.0729
Towards a science of security games
Abstract. Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the impor-tance of different targets, the responses of the adversaries to the security posture, and the potential uncertainties in adversary payoffs and observations, etc. Com-putational game theory can help generate such security schedules. Indeed, casting the problem as a Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for scheduling of secu-rity resources. These applications are leading to real-world use-inspired research in the emerging research area of “security games”. The research challenges posed by these applications include scaling up security games to real-world sized prob-lems, handling multiple types of uncertainty, and dealing with bounded rationality of human adversaries.
Stochastic Stackelberg games
In this paper, we consider a discrete-time stochastic Stackelberg game where
there is a defender (also called leader) who has to defend a target and an
attacker (also called follower). Both attacker and defender have conditionally
independent private types, conditioned on action and previous state, that
evolve as controlled Markov processes. The objective is to compute the
stochastic Stackelberg equilibrium of the game where defender commits to a
strategy. The attacker's strategy is the best response to the defender strategy
and defender's strategy is optimum given the attacker plays the best response.
In general, computing such equilibrium involves solving a fixed-point equation
for the whole game. In this paper, we present an algorithm that computes such
strategies by solving smaller fixed-point equations for each time . This
reduces the computational complexity of the problem from double exponential in
time to linear in time. Based on this algorithm, we compute stochastic
Stackelberg equilibrium of a security example.Comment: 31 pages, 6 figure
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