Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Playing games for security: an efficient exact algorithm for solving Bayesian Stackelberg games
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Leader-follower strategies for robotic patrolling in environments with arbitrary topologies
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Computing optimal randomized resource allocations for massive security games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
GUARDS and PROTECT: next generation applications of security games
ACM SIGecom Exchanges
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Security games with multiple attacker resources
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Approximation-guided evolutionary multi-objective optimization
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Game theory for security: an important challenge for multiagent systems
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
An extended study on multi-objective security games
Autonomous Agents and Multi-Agent Systems
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The burgeoning area of security games has focused on real-world domains where security agencies protect critical infrastructure from a diverse set of adaptive adversaries. There are security domains where the payoffs for preventing the different types of adversaries may take different forms (seized money, reduced crime, saved lives, etc) which are not readily comparable. Thus, it can be difficult to know how to weigh the different payoffs when deciding on a security strategy. To address the challenges of these domains, we propose a fundamentally different solution concept, multi-objective security games (MOSG), which combines security games and multi-objective optimization. Instead of a single optimal solution, MOSGs have a set of Pareto optimal (non-dominated) solutions referred to as the Pareto frontier. The Pareto frontier can be generated by solving a sequence of constrained single-objective optimization problems (CSOP), where one objective is selected to be maximized while lower bounds are specified for the other objectives. Our contributions include: (i) an algorithm, Iterative ε-Constraints, for generating the sequence of CSOPs; (ii) an exact approach for solving an MILP formulation of a CSOP (which also applies to multi-objective optimization in more general Stackelberg games); (iii) heuristics that achieve speedup by exploiting the structure of security games to further constrain a CSOP; (iv) an approximate approach for solving an algorithmic formulation of a CSOP, increasing the scalability of our approach with quality guarantees. Additional contributions of this paper include proofs on the level of approximation and detailed experimental evaluation of the proposed approaches.