Lanthorn, vol. 8, no. 30, May 27, 1976

Lanthorn is Grand Valley State\u27s student newspaper, published from 1968 to the present

Computing Correlated Equilibria in Partially Observable Stochastic Games

V reálném světě se musíme vypořádávat se situacemi vyžadujícími kooperaci zúčastněných agentů při zachování jejich racionality. Takovéto problémy odpovídají herně teoretickému konceptu korelovaného ekvilibria. Existuje několik prací, které se zabývají výpočtem korelovaného equilibria v stochastických hrách. V současnosti však neexistuje žádný algoritmus, který by byl schopen počítat korelované ekvilibrium pro obecné částečně pozorovatelné stochastické hry. V této práci představujeme první algoritmus pro aproximaci korelovaného ekvilibria v částečně pozorovatelných stochastických hrách, který řeší tyto hry iterativně pomocí postupného zvětšování vygenerované podmnožiny belief stavů. Přestože náš algoritmus nemá žádné garance optimality, ukazujeme, že je schopen nalézt přijatelná řešení.In the real world, we have to deal with situations requiring cooperation of participating agents keeping their rationality. These problems are addressed by the game theoretical concept of correlated equilibrium. There are some works focusing on the problem of computing correlated equilibria in stochastic games. So far there is no algorithm capable of computing correlated equilibria in general partially observable stochastic games. In this work, we propose the first algorithm for approximating correlated equilibria in partially observable stochastic games that iteratively solves these games using gradually expanding generated subset of belifestates. Even though the algorithm has no optimality guarantees, we show that it is capable to compute reasonable solutions

Games of Pursuit-Evasion with Multiple Agents and Subject to Uncertainties

Over the past decade, there have been constant efforts to induct unmanned aerial vehicles (UAVs) into military engagements, disaster management, weather monitoring, and package delivery, among various other applications. With UAVs starting to come out of controlled environments into real-world scenarios, uncertainties that can be either exogenous or endogenous play an important role in the planning and decision-making aspects of deploying UAVs. At the same time, while the demand for UAVs is steadily increasing, major governments are working on their regulations. There is an urgency to design surveillance and security systems that can efficiently regulate the traffic and usage of these UAVs, especially in secured airspaces. With this motivation, the thesis primarily focuses on airspace security, providing solutions for safe planning under uncertainties while addressing aspects concerning target acquisition and collision avoidance. In this thesis, we first present our work on solutions developed for airspace security that employ multiple agents to capture multiple targets in an efficient manner. Since multi-pursuer multi-evader problems are known to be intractable, heuristics based on the geometry of the game are employed to obtain task-allocation algorithms that are computationally efficient. This is achieved by first analyzing pursuit-evasion problems involving two pursuers and one evader. Using the insights obtained from this analysis, a dynamic allocation algorithm for the pursuers, which is independent of the evader's strategy, is proposed. The algorithm is further extended to solve multi-pursuer multi-evader problems for any number of pursuers and evaders, assuming both sets of agents to be heterogeneous in terms of speed capabilities. Next, we consider stochastic disturbances, analyzing pursuit-evasion problems under stochastic flow fields using forward reachability analysis, and covariance steering. The problem of steering a Gaussian in adversarial scenarios is first analyzed under the framework of general constrained games. The resulting covariance steering problem is solved numerically using iterative techniques. The proposed approach is applied to the missile endgame guidance problem. Subsequently, using the theory of covariance steering, an approach to solve pursuit-evasion problems under external stochastic flow fields is discussed. Assuming a linear feedback control strategy, a chance-constrained covariance game is constructed around the nominal solution of the players. The proposed approach is tested on realistic linear and nonlinear flow fields. Numerical simulations suggest that the pursuer can effectively steer the game towards capture. Finally, the uncertainties are assumed to be parametric in nature. To this end, we first formalize optimal control under parametric uncertainties while introducing sensitivity functions and costates based techniques to address robustness under parametric variations. Utilizing the sensitivity functions, we address the problem of safe path planning in environments containing dynamic obstacles with an uncertain motion model. The sensitivity function based-approach is then extended to address game-theoretic formulations that resemble a "fog of war" situation.Ph.D

Spartan Daily, May 10, 1988

Volume 90, Issue 61https://scholarworks.sjsu.edu/spartandaily/7720/thumbnail.jp