An evolutionary behavioral model for decision making

Romero Lopez, Dr Oscar Javier (2011) An evolutionary behavioral model for decision making. [Journal (Paginated)]

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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 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) the same evolutionary mechanism is able to assemble quite complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies.

Item Type:Journal (Paginated)
Keywords:Intelligent and autonomous agents, adaptive behavior, automated planning, behavior networks, evolutionary computation, gene expression programming
Subjects:Biology > Behavioral Biology
Computer Science > Artificial Intelligence
Computer Science > Complexity Theory
Computer Science > Machine Learning
Computer Science > Robotics
ID Code:8015
Deposited By: Romero López, Dr. Oscar J.
Deposited On:09 Nov 2012 19:23
Last Modified:09 Nov 2012 19:23

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Booker L, Goldberg D, Holland J (1989) Classifier systems and genetic algorithms. Artificial Intelligence

Butz MV, Goldberg DE, Stolzmann W (2002) The anticipatory classifier system and genetic generalization. Natural Computing 1:427-467

De Jong E, Pollack J (2004) Ideal evaluation from coevolution, evolutionary computation. Evol Computation 12(2):159-192

Dorer K (1999) Behavior networks for continuous domains using situation-dependent motiva tions. In: Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2, pp 1233-1238

Dorer K (2004) Extended behavior networks for behavior selection in dynamic and continuous domains. Proceedings of ECAI-04 Workshop on Agents in dynamic and real-time environments

Ferreira C (2000) Genetic representation and genetic neutrality in gene expression program ming. Advances in Complex Systems 5(4):389-408

Ferreira C (2001) Gene expression programming: A new adaptive algorithm for solving problems, complex systems. Cognitive Science 13:87-129

Ferreira C (2006) Automatically defined functions in gene expression programming. Com putational Intelligence 13:21-56

Franklin S (2006) The LIDA architecture: Adding new modes of learning to an intelligent, autonomous, software agent. In Proc. of the Int. Conf. on Integrated Design and Process Technology, Cambridge

Koza J (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press

Maes P (1989) How to do the right thing. Connection Science Journal 1:291-323

Maes P (1990) Situated agents can have goals. Robotics and Autonomous Systems 6(1):49-70

Maes P (1991) The agent network architecture (ana). SIGART Bull 2:115-120

Maes P (1992) Learning behavior networks from experience. In: Proceedings of the First Eu ropean Conference on Artificial Life, pp 48-57

Nebel B, Babovich-Lierler Y (2004) When are behaviour networks well behaved? In: Proceedings of ECAI, pp 672-676

Poli R, Langdon W, McPhee N (2008) A Field Guide to Genetic Programming. Standford University, Massachusetts, USA

Romero O, de Antonio A (2008) Bio-inspired cognitive architecture for adap tive agents based on an evolutionary approach. Adaptive Learning Agents and Multi-Agent Systems, ALAMAS+ALAg pp 68-72

Romero O, de Antonio A (2009a) Hybridization of cognitive models using evolutionary strategies. IEEE Congress on Evolutionary Computation CEC 09 pp 89-96

Romero O, de Antonio A (2009b) Modulation of multi-level evolutionary strategies for artificial cognition. In Proc Genetic and Evolutionary Computation Conference, GECCO pp 279-292

Sacerdoti E (1977) A Structure for Plans and Behavior. Elsevier, North Holland

Shanahan MP, Baars B (2005) Applying global workspace theory to the frame problem. Cognition 98(2):157-176

Stolzmann W (1999) Latent learning in khepera robots with anticipatory classifier sys tems. Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop Program pp 290-297

Sussman G (1975) A computer model of skill acquisition. PhD thesis, Elsevier Science Inc.


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