We study pursuit-evasion games for mobile robots and their applications to collision avoidance. In the first part of the paper, under the assumption that the pursuer and the evader (possibly subject to physical constraints) share the same roadmap to plan their strategies, we present sound and complete strategies for three different games. In the second part, we utilize the pursuit-evasion resul...

Article history: Received 9 July 2011 Received in revised form 4 November 2013 Accepted 1 December 2013 Communicated by D. Peleg

In this paper, we consider the design and implementation of practical pursuit-evasion games with networked robots, where a communication network provides sensing-at-a-distance as well as a communication backbone that enables tighter coordination between pursuers. We first develop, using the theory of zero-sum This material is based in part upon work supported by the National Science Foundation ...

Dynamic games (DGs) play an important role in distributed decision making and control in complex environments. Finding optimal/approximate solutions for these games in the imperfect-information setting is currently a challenge for mathematicians and computer scientists, especially when state and action spaces are infinite. This paper presents an approach to this problem by using multi-agent rei...

This paper presents a model-based approximate λ-policy iteration approach using temporal differences for optimizing paths online for a pursuit-evasion problem, where an agent must visit several target positions within a region of interest while simultaneously avoiding one or more actively pursuing adversaries. This method is relevant to applications, such as robotic path planning, mobile-sensor...

We introduce a problem set-up we call the Iterated Matching Pennies (IMP) game and show that it is a powerful framework for the study of three problems: adversarial learnability, conventional (i.e., non-adversarial) learnability and approximability. Using it, we are able to derive the following theorems. (1) It is possible to learn by example all of Σ1 ∪ Π1 as well as some supersets; (2) in adv...

Dynamic zero-sum games are an important class of problems with applications ranging from evasion-pursuit and heads-up poker to certain adversarial versions of control problems such as multi-armed bandit and multiclass queuing problems. These games are generally very difficult to solve even when one player’s strategy is fixed, and so constructing and evaluating good sub-optimal policies for each...