An evolutionary behavioral model for decision making

For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud the same evolutionary mechanism is able to assemble quite\ud complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies

Modulation of multi-level evolutionary strategies for artificial cognition

There are several theories of cognition, each taking a different position on the nature of cognition, what a cognitive system should do, and how a cognitive system should be analyzed and synthesized. From these, it is possible to discern three broad classes: the cognitivist approach based on symbolic information processing representational systems; the emergent systems approach embracing connectionist systems, dynamical systems, and enactive systems, all based on a lesser or greater extent of principles of self-organization, and the hybrid approach which combine the best of the emergent systems and cognitivist systems. Our research focuses on implementing a hybrid architecture for cognitive agents supported by both cognitivist and emergent approaches. On the one hand, the cognitivist approach provides an explicit knowledge representation through the use of symbolic AI techniques. On the other hand, the emergent approach defines three evolutionary strategies as observed in nature: Epigenesis, Ontogenesis, and Phylogenesis, endowing the architecture with implicit knowledge learning, sub-symbolic representations, and emergent behavior guided by bio-inspired computational intelligence techniques