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By Javier Insa-Cabrera, David L. Dowe, José Hernández-Orallo (auth.), Jose A. Lozano, José A. Gámez, José A. Moreno (eds.)

ISBN-10: 3642252737

ISBN-13: 9783642252730

The two-volume set LNAI 7094 and LNAI 7095 constitutes the refereed lawsuits of the tenth Mexican overseas convention on synthetic Intelligence, MICAI 2011, held in Puebla, Mexico, in November/December 2011. The ninety six revised papers awarded have been conscientiously reviewed and chosen from a number of submissions. the 1st quantity comprises 50 papers representing the present major subject matters of curiosity for the AI neighborhood and their functions. The papers are prepared within the following topical sections: automatic reasoning and multi-agent platforms; challenge fixing and desktop studying; traditional language processing; robotics, making plans and scheduling; and scientific functions of synthetic intelligence.

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Extra resources for Advances in Artificial Intelligence: 14th Conference of the Spanish Association for Artificial Intelligence, Caepia 2011, La Laguna, Spain, November 7-11, 2011. Proceedings

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Our results suggest that, even without perfect information or explicit optimization, it is possible for the economy to converge towards the analytically optimal state. Keywords: Agent-based computational economics, market selforganisation, reinforcement learning, regression learning. 1 Introduction Ever since the introduction of the concept of competitive market equilibrium by Leon Walras [14], economists have thought about the question how markets could eventually get to this state. And indeed, this is a reasonable question, since economic equilibrium is defined as a set of prices and endowments, such that every market participant is maximizing her profit or utility.

The main goal of IM is to restore a normal service operation as quickly as possible with the least possible impact on either the business or the user, at a cost-effective price, thus ensuring that the best possible levels of service quality and availability are maintained. This process is referred to as the Incident Management Lifecycle. Normal service operation is defined here as service operation within a Service Level Agreement (SLA) [7], which establishes bounds about the average time to solve an incident.

J} be the sequence of goods consumed by i i agent i in period t, a total of n. Furthermore, let uit,n := Ut,n − Ut,n−1 be the i realized marginal utility of agent i in period t, where Ut,n is the utility obtained i . In words, uit,n is the increase of the overall utility from the consumption of Jt,n i level of one agent after having consumed one more good, namely good jt,n . Then, the propensity of choosing good j for consumption next is ⎧ uit,n ⎪ i i ⎪ ⎨ rt,n−1,j γ¯ + α jt,n =j i p t,j 0 < γ¯ < 1.

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Advances in Artificial Intelligence: 14th Conference of the Spanish Association for Artificial Intelligence, Caepia 2011, La Laguna, Spain, November 7-11, 2011. Proceedings by Javier Insa-Cabrera, David L. Dowe, José Hernández-Orallo (auth.), Jose A. Lozano, José A. Gámez, José A. Moreno (eds.)


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