Hide-and-Seek with Directional Sensing

We consider a game played between a hider, who hides a static object in one of several possible positions in a bounded planar region, and a searcher, who wishes to reach the object by querying sensors placed in the plane. The searcher is a mobile agent, and whenever it physically visits a sensor, the sensor returns a random direction, corresponding to a half-plane in which the hidden object is located. We first present a novel search heuristic and characterize bounds on the expected distance covered before reaching the object. Next, we model this game as a large-dimensional zero-sum dynamic game and we apply a recently introduced randomized sampling technique that provides a probabilistic level of security to the hider. We observe that, when the randomized sampling approach is only allowed to select a very small number of samples, the cost of the heuristic is comparable to the security level provided by the randomized procedure. However, as we allow the number of samples to increase, the randomized procedure provides a higher probabilistic security level.Comment: A short version of this paper (without proofs) will be presented at the 18th IFAC World Congress (IFAC 2011), Milan (Italy), August 28-September 2, 201

Capturing an Evader Using Multiple Pursuers with Sensing Limitations in Convex Environment

A modified continuous-time pursuit-evasion game with multiple pursuers and a single evader is studied. The game has been played in an obstacle-free convex environment which consists an exit gate through which the evader may escape. The geometry of the convex is unknown to all players except pursuers know the location of the exit gate and they can communicate with each other. All players have equal maximum velocities and identical sensing range. An evader is navigating inside the environment and seeking the exit gate to win the game. A novel sweep-pursuit-capture strategy for the pursuers to search and capture the evader under some necessary and sufficient conditions is presented. We also show that three pursuers are sufficient to finish the operation successfully. Non-holonomic wheeled mobile robots of the same configurations have been used as the pursuers and the evader. Simulation studies demonstrate the performance of the proposed strategy in terms of interception time and the distance traveled by the players.

Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning

A large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference learning framework. Specifically, a preference learning problem is seen as a two-players zero-sum game. An algorithm is proposed to incrementally include new useful features into the hypothesis. This can be particularly important when dealing with a very large number of potential features like, for instance, in relational learning and rule extraction. A game theoretical analysis is used to demonstrate the convergence of the algorithm. Furthermore, leveraging on the natural analogy between features and rules, the resulting models can be easily interpreted by humans. An extensive set of experiments on classification tasks shows the effectiveness of the proposed method in terms of interpretability and feature selection quality, with accuracy at the state-of-the-art.Comment: AAAI 201

Incentive-Based Control of Asynchronous Best-Response Dynamics on Binary Decision Networks

Various populations of interacting decision-making agents can be modeled by asynchronous best-response dynamics, or equivalently, linear threshold dynamics. Building upon recent convergence results in the absence of control, we now consider how such a network can be efficiently driven to a desired equilibrium state by offering payoff incentives or rewards for using a particular strategy, either uniformly or targeted to individuals. We begin by showing that strategy changes are monotone following an increase in payoffs in coordination games, and that the resulting equilibrium is unique. Based on these results, for the case when a uniform incentive is offered to all agents, we show how to compute the optimal incentive using a binary search algorithm. When different incentives can be offered to each agent, we propose a new algorithm to select which agents should be targeted based on maximizing a ratio between the cascading effect of a strategy switch by each agent and the incentive required to cause the agent to switch. Simulations show that this algorithm computes near-optimal targeted incentives for a wide range of networks and payoff distributions in coordination games and can also be effective for anti-coordination games

A Centrality-Based Security Game for Multi-Hop Networks

We formulate a network security problem as a zero-sum game between an attacker who tries to disrupt a network by disabling one or more nodes, and the nodes of the network who must allocate limited resources in defense of the network. The utility of the zero-sum game can be one of several network performance metrics that correspond to node centrality measures. We first present a fast centralized algorithm that uses a monotone property of the utility function to compute saddle-point equilibrium strategies for the case of single-node attacks and single- or multiple-node defense. We then extend the approach to the distributed setting by computing the necessary quantities using a finite-time distributed averaging algorithm. For simultaneous attacks to multiple nodes the computational complexity becomes quite high, so we propose a method to approximate the saddle-point equilibrium strategies based on a sequential simplification, which performs well in simulations

Optimal Defensive Resource Allocation for a Centrality-Based Security Game on Multi-Hop Networks

We present a new analysis of multi-hop network security in the form of a zero-sum game played between an attacker who tries to disrupt a network by disabling one or more nodes, and the nodes of the network who must allocate limited resources in defense of the network. The payoffs in the zero-sum game can be one of several performance metrics that correspond to node centrality measures. In the case of single-node attacks, we use a monotonicity property of the mixed attack strategies to construct a simple and very fast algorithm to compute saddle-point equilibrium strategies for both single-node and multiple-node defense. For simultaneous multiple-node attacks on large networks, the computational complexity becomes quite high, so we present a method to approximate the equilibrium strategies based on a sequential simplification, which performs well in simulations

A priori data-driven robustness guarantees on strategic deviations from generalised Nash equilibria

In this paper we focus on noncooperative games with uncertain constraints coupling the agents’ decisions. We consider a setting where bounded deviations of agents’ decisions from the equilibrium are possible, and uncertain constraints are inferred from data. Building upon recent advances in the so called scenario approach, we propose a randomised algorithm that returns a nominal equilibrium such that a pre-specified bound on the probability of violation for yet unseen constraints is satisfied for an entire region of admissible deviations surrounding it—thus supporting neighbourhoods of equilibria with probabilistic feasibility certificates. For the case in which the game admits a potential function, whose minimum coincides with the social welfare optimum of the population, the proposed algorithmic scheme opens the road to achieve a trade-off between the guaranteed feasibility levels of the region surrounding the nominal equilibrium, and its system-level efficiency. Detailed numerical simulations corroborate our theoretical results