A Case-Based Reasoning Architecture of an Hybrid Software Agent

Software agents represent an evolution of traditional software, having the ability to control their own behavior and acting with autonomy. Typically, software agents act reactively, where actions and perceptions are predefined at design time, or in a deliberative manner, where the corresponding action for a given perception is found at run time through a process of reasoning. However, especially in dynamic environments, to perform better, software agents have to act using both forms of behavior and also have learning abilities to follow up changes in the environment. Case-based reasoning is a technique that uses previous experience to solve new problems. This kind of reasoning is particularly appropriate for learning, since learning is already part of the life cycle of case-based reasoning. This paper proposes a hybrid software architecture that combines case-based reasoning, reactive behavior and learning. The main distinguishing feature of the proposal is the learning of reactive behavior, more quickly and efficiently, through continuous interactions of the agent with its environment. Currently, a software agent based on this architecture is being developed in order to evaluate it.