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This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve accuracy in the action selection process, CBRetaliate uses CBR to allow RL to respond more quickly to changing conditions. CBRetaliate combines two key features: it uses a time window to compute similarity and stores and...
In this chapter, we present an overview of several techniques we have studied over the years to build game AI for domination games. Domination is a game style in which teams compete for control of map locations and has been very popular over the years. Due to the rules of the games, good performance is mostly dependent on overall strategy rather than the skill of individual team members. Hence, this...
Existing algorithms for maritime threat detection employ a variety of normalcy models that are probabilistic and/or rule-based. Unfortunately, they can be limited in their ability to model the subtlety and complexity of multiple vessel types and their spatio-temporal events, yet their representation is needed to accurately detect anomalies in maritime scenarios. To address these limitations, we apply...
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