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
Case-based reasoning (CBR) approaches often use extensions of build trees as state lattices (and sets of tactics for each state) as for @cite_1 @cite_4 in Wargus. OntanonCBR ( OntanonCBR ) base their real-time case-based planning (CBP) system on a plan dependency graph which is learned from human demonstration in Wargus. In @cite_7 , they use situation assessment for plan retrieval'' from annotated replays, which recognizes distance to behaviors (a goal and a plan), and selected only the low-level features with the higher information gain. HsiehS08 ( HsiehS08 ) based their work on @cite_1 and used StarCraft replays to construct states and building sequences. Strategies are choices of building construction order in their model.
@cite_1 @cite_7 @cite_4
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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. In recent years, several researchers have studied the suitability of CBR to cope with dynamic or continuous or temporal domains. In these domains, the current state depends on the past temporal states. This feature really makes difficult to cope with these domains. This means that classical individual case retrieval is not very accurate, as the dynamic domain is structured in a temporally related stream of cases rather than in single cases. The CBR system solutions should also be dynamic and continuous, and temporal dependencies among cases should be taken into account. This paper proposes a new approach and a new framework to develop temporal CBR systems: Episode-Based Reasoning. It is based on the abstraction of temporal sequences of cases, which are named as episodes. Our preliminary evaluation in the wastewater treatment plants domain shows that Episode-Based Reasoning seems to outperform classical CBR systems. 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. This paper is a continuation of our recent work on preference-based CBR, or Pref-CBR for short. The latter is conceived as a case-based reasoning methodology in which problem solving experience is represented in the form of contextualized preferences, namely preferences for candidate solutions in the context of a target problem to be solved. In our Pref-CBR framework, case-based problem solving is formalized as a preference-guided search process in the space of candidate solutions, which is equipped with a similarity (or, equivalently, a distance) measure. Since the efficacy of Pref-CBR is influenced by the adequacy of this measure, we propose a learning method for adapting solution similarity on the basis of experience gathered by the CBR system in the course of time. More specifically, our method makes use of an underlying probabilistic model and realizes adaptation as Bayesian inference. The effectiveness of this method is illustrated in a case study that deals with the case-based recommendation of red wines.