Search and recall for RTS tactical scenarios

Jason Traish, James Tulip, Wayne Moore

Research output: Book chapter/Published conference paperConference paperpeer-review


The success of a Real-Time Strategy agent is heavily dependent on its ability to respond well to a large number of diverse tactical situations. We present a novel method of tactical decision making called Search and Recall (S&R) which is a hybrid of Search and Case Based Reasoning (CBR) methods. S&R allows an agent to learn and retain strategies discovered over the agent's history of play, and to adapt quickly in novel circumstances. The sense of memory that S&R provides an RTS AI agent allows it to improve its performance over time as better responses are discovered. S&R demonstrates an minimum win rate of 92% in standard scenarios evaluated in this paper. S&R decouples search from the main game loop which allows arbitrary computational complexity and execution time for search simulations. Meanwhile in-game decision making is based on CBR and remains fast and simple. This paper presents an S&R model which extends the ability of an RTS AI agent to deal with complex tactical situations. These situations include special unit abilities, fog of war, path finding, collision detection and terrain analysis.
Original languageEnglish
Title of host publicationAISB Convention 2015
Place of PublicationUnited Kingdom
PublisherThe Society for the Study of Artificial Intelligence and the Simulation of Behaviour
Number of pages6
Publication statusPublished - 2015
EventArtifical Intelligence and Simulation of Behaviour Convention - University of Kent, Canterbury; United Kingdom, United Kingdom
Duration: 20 Apr 201522 Apr 2015


ConferenceArtifical Intelligence and Simulation of Behaviour Convention
Country/TerritoryUnited Kingdom


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