This paper advocates the exploration of the full state of recorded real-time strategy (RTS) games, by human or robotic players, to discover how to reason about tactics and strategy. We present a dataset of StarCraft games encompassing the most of the games' state (not only player's orders). We explain one of the possible usages of this dataset by clustering armies on their compositions. This reduction of armies compositions to mixtures of Gaussian allow for strategic reasoning at the level of the components. We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components
While several researchers have applied case-based reasoning techniques to games, only Ponsen and Spronck (2004) have addressed the challenging problem of learning to win real-time games. Focusing on Wargus, they report good results for a genetic algorithm that searches in plan space, and for a weighting algorithm (dynamic scripting) that biases subplan retrieval. However, both approaches assume a static opponent, and were not designed to transfer their learned knowledge to opponents with substantially different strategies. We introduce a plan retrieval algorithm that, by using three key sources of domain knowledge, removes the assumption of a static opponent. Our experiments show that its implementation in the Case-based Tactician (CaT) significantly outperforms the best among a set of genetically evolved plans when tested against random Wargus opponents. CaT communicates with Wargus through TIELT, a testbed for integrating and evaluating decision systems with simulators. This is the first application of TIELT. We describe this application, our lessons learned, and our motivations for future work. Case-Based Planning (CBP) is an effective technique for solving planning problems that has the potential to reduce the computational complexity of the generative planning approaches [8,3]. However, the success of plan execution using CBP depends highly on the selection of a correct plan; especially when the case-base of plans is extensive. In this paper we introduce the concept of a situationand explain a situation assessmentalgorithm which improves plan retrieval for CBP. We have applied situation assessment to our previous CBP system, Darmok [11], in the domain of real-time strategy games. During Darmok's execution using situation assessment, the high-level representation of the game state i.e. situation is predicted using a decision tree based Situation-Classification model. Situation predicted is further used for the selection of relevant knowledge intensive features, which are derived from the basic representation of the game state, to compute the similarity of cases with the current problem. The feature selection performed here is knowledge based and improves the performance of similarity measurements during plan retrieval. The instantiation of the situation assessment algorithm to Darmok gave us promising results for plan retrieval within the real-time constraints. Spatial reasoning is a major challenge for strategy-game artificial intelligence systems. Qualitative spatial reasoning techniques can help overcome this challenge by providing more expressive spatial representations, better communication of intent, better path-finding and reusable strategy libraries.