Algorithms and Decision-Making Processes in Distributed Attacker-Defender Game

该项目将考虑一些来自空战领域的任务，并将这些任务映射到更简单/抽象的“规范”问题;这些问题将成为早期研究的重点。理想情况下，研究将产生的方法/算法，可以有效地映射到说明复杂的空战情况下，以后的研究将集中在调查这些在一个合适的配置游戏衍生simulation.Further，这些问题的解决方案在一个复杂的，不确定的和动态的情况下，实时是一个具有挑战性的计算任务。对于具有代表性的复杂性的问题，这可能需要应用分布式/去中心化的高性能计算方法。计算游戏提供了一个强大的抽象框架来建模和分析具有不可控对手或自然的交互过程。抽象表示可以揭示现有策略的弱点，并允许开发具有数学保证的新策略。对于许多攻击者-防守者游戏，检查获胜策略的存在在计算上是困难的，甚至是不可判定的。对目标和玩家的移动/动作的各种限制可以显着改变问题的计算复杂性。该项目将专注于开发理论计算机科学技术，通过开发近似算法，应用机器学习技术以及解决不同离散领域和几何环境下的策略优化和组合问题来克服计算限制。

The project will consider a few missions from the air combat domain and map these to simpler/abstracted 'canonical' problems; these will form the focus for the early research. Ideally, the research will yield methods/algorithms that can be usefully mapped across to illustratively complex air-combat situations; later research will then focus on investigating these within a suitable configured game-derived simulation.Further, the solution of these problems in a complex, uncertain and dynamic situation in real-time is a challenging computational task. For problems of representative complexity, this will therefore likely require an application of distributed/decentralised high-performance computing methods.Computational games provide a powerful abstract framework to model and analyse interactive processes with uncontrollable adversaries or a nature. Abstract representation can reveal the weaknesses in existing strategies and allow to develop new strategies with a mathematical guarantee. For many Attacker-Defender games it can be computationally hard or even undecidable to check the existence of a winning strategy. Various restrictions on the objectives and player's moves/actions can significantly change the computational complexity of the problems. It creates the scope for research on the design of algorithms to verify the existence of the winning strategies or to design new strategies.The project will focus on developing Theoretical Computer Science techniques to overcome computational constraints by developing approximation algorithms, applying machine learning techniques and solving strategic optimisation and combinatorial problems on different discrete arenas and geometric environments.