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By Roque Marín, Eva Onaindía, Alberto Bugarín, José Santos
This publication constitutes the completely referred post-proceedings of the eleventh convention of the Spanish organization for man made Intelligence, CAEPIA 2005, held in Santiago de Compostela, Spain in November 2005.
The forty eight revised complete papers provided including an invited paper have been rigorously chosen in the course of rounds of reviewing and development from an preliminary overall of 147 submissions. The papers span the full spectrum of man-made intelligence from foundational and theoretical concerns to complicated purposes in a variety of fields.
Read Online or Download Current Topics in Artificial Intelligence: 11th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2005, Santiago de Compostela, PDF
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Example text
M ) is called the vector of surrogate multipliers. The surrogate instance for K, denoted by SR(K, ω), is defined as the 0–1 one-dimensional knapsack problem given by: SR(K, ω) = (n, 1, p, ω · A, ω · b ) (6) We shall denote by αopt SR(K,ω) the solution of the surrogate instance SR(K, ω). L. Alonso, F. L. Monta˜ na Remark 5. The best possible upper bound for f (αopt K ) using the inequality 7 is + m computed by finding the minimum value min{f (αopt SR(K,ω) ) : ω ∈ (IR ) }. Next proposition motivates the genetic strategy to approximate the optimal values of the surrogate multipliers ω.
In IPMU2004, pages 183–191, 2004. 6. LA. Zadeh. The concept of a linguistic variable and its application to approximate reasoning. Information Science, 8:199–249, 1975. es Abstract. This paper defines a meta-reasoning model for real-time agents. The purpose of this model is to increase the adaptability in this kind of agents. This model allows to adapt the agent’s behavior as well as the agent’s own reasoning process. The application of this model to an specific real-time agent architecture is also presented.
Once all level 1 MFTPs have been completed, we go up to the next level and resolve its MFTPs by assembling solutions for its subMFTPs and assignations to points by means of expressions equivalent to 3 and 2, respectively, but which are defined between the nesting levels l and l − 1. The process finishes when the global pattern is resolved. Figure 2b shows a procedure that searches for solutions for an MFTP by extending a locally valid tuple of assignations. The list Lr stores the solutions of the next subMFTP, or assignations to the next significant point, whose degree of local consistency with respect to the tuple that has been assembled up to this point is higher than cinf ; the list HM stores solutions found for the MFTP M and its degree of satisfaction; n is M the number of significant points of M; nMh is the number of significant points m MhM of MM .