Novel Approaches to Stochastic Pursuit-Evasion Differential Games with multiple players

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-05-C-0018
Agency Tracking Number: A043-061-2987
Amount: $120,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2005
Solicitation Year: 2004
Solicitation Topic Code: A04-061
Solicitation Number: 2004.3
Small Business Information
INTELLIGENT AUTOMATION, INC.
15400 Calhoun Drive, Suite 400, Rockville, MD, 20855
DUNS: 161911532
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Chiman Kwan
 Vice President of R & D
 (301) 294-5238
 ckwan@i-a-i.com
Business Contact
 Mark James
Title: Contract Manager
Phone: (301) 294-5221
Email: mjames@i-a-i.com
Research Institution
N/A
Abstract
We propose to systemically explore differential pursuit-evasion games with multiple pursuers and evaders in continuous time and in random environment. We start with the simplest case with the assumptions of perfect information and common knowledge. The approach is a direct extension of Isaacs's method for differential games with a single pursuer and evader, where the concept of saddle point solutions is extended. Second, the assumption of perfect information is relaxed to that of complete observability. To simplify the theoretical analysis, a transformation of the objective function is considered such that the linear quadratic dynamic game theory can be applied directly. Asymptotic Nash equilibrium solutions can be easily determined in this case. Third, the assumptions of observability and common knowledge are further relaxed, the learning theory in games is proposed. In this case, a larger set of self-confirming equilibrium solutions is used to instead of Nash equilibrium. Fourth, for the situation that players cannot predict others' strategies, a decentralized objective function is constructed for each pursuer, and the maxmin strategy is proposed. The coordination control is achieved by using maximal Nash equilibrium solution among those distributed pursuers. Finally, a general nonlinear filter is proposed for each pursuer to estimate the observable state variables in noisy environment.

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