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

Hybridization of cognitive models using evolutionary strategies

Incorporating different kinds of micro-theories of cognition and modulating several mechanisms to unify all the recommended actions and outputs of an Intelligent System when a huge amount of environmental variables are changing continuously with increasing complexity, may become a very comprehensive task. The presented framework proposes an Hybrid Cognitive Architecture that relies on integrating of emergent systems approaches —connectionist and autopoietic systems—, and cognitivist approaches, in order to combine implicit and explicit processes necessary in developing cognitive skills. The proposed architecture includes different kinds of learning capabilities at each cognitive level which grant to the architecture a big plasticity. In addition, the propounded attention module includes an evolutionary mechanism based on gene expression programming to evolve a set of eligibility conditions in charge of modulating the coalition/ subordination of specialized behaviours, taking into consideration the theatre metaphor for consciousness. Finally, a co-evolutionary mechanism is proposed to propagate behaviours and knowledge between cognitive systems —Agents— on the basis of memetic engineering. The proposed architecture was proved in an animat environment using a multi-agent platform where several emergent properties of self-organization arose