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<title>Congreso de Inteligencia Computacional Aplicada (CICA)</title>
<link>http://hdl.handle.net/10226/266</link>
<description>Colección de presentaciones al congreso CICA 2009 realizado en la Universidad de Palermo.</description>
<pubDate>Wed, 29 Apr 2026 03:49:10 GMT</pubDate>
<dc:date>2026-04-29T03:49:10Z</dc:date>
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<title>Optimization and Selection of Alternative Modular Structures using Genetic Algorithms</title>
<link>http://hdl.handle.net/10226/476</link>
<description>Optimization and Selection of Alternative Modular Structures using Genetic Algorithms
Durán, Orlando; Perez, Luis; Rodriguez, Nibaldo
In most transformations from dedicated to modular approaches, products are assumed to have a unique modular&#13;
structure. However, it is well known that alternatives for constructing modular structures may exist in any level of&#13;
abstraction. Explicit considerations of alternative structures invoke changes in the number of module instances so&#13;
that lower capital investment in modules, more independency of structures and higher efficiency can be achieved.&#13;
Relatively few research papers that deal with the optimization of modular structures problem with alternative&#13;
assembly combinations aiming at minimization of module investments were found in the literature. A genetic&#13;
algorithm (GA) was applied to solve the optimization problem of selecting and combining the alternative of modular&#13;
structures to create a set of modular structures minimizing the cost involved in its implementation. Test results are&#13;
presented and the performance of the proposed GA is compared with solutions obtained from total enumeration&#13;
tests.
</description>
<pubDate>Thu, 17 Sep 2009 19:27:14 GMT</pubDate>
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<dc:date>2009-09-17T19:27:14Z</dc:date>
</item>
<item>
<title>Wavelet Denoising Based Multivariate Polynomial For Anchovy Catches Forecasting</title>
<link>http://hdl.handle.net/10226/475</link>
<description>Wavelet Denoising Based Multivariate Polynomial For Anchovy Catches Forecasting
Rodriguez, Nibaldo; Cabrera, Guillermo
In this paprer, a multivariate polynomial (MP) combined with denoising techniques is proposed to forecast&#13;
1-month ahead monthly anchovy catches in the north area of Chile. The anchovy catches data is denoised&#13;
by using discrete stationary wavelet transform and then appropriate is used as inputs to the MP. The MP's&#13;
parameters are estimated using the penalized least square (LS) method and the performance evaluation of&#13;
the proposed forecaster showed that a 98% of the explained variance was captured with a reduced parsimony.
</description>
<pubDate>Thu, 17 Sep 2009 19:27:08 GMT</pubDate>
<guid isPermaLink="false">http://hdl.handle.net/10226/475</guid>
<dc:date>2009-09-17T19:27:08Z</dc:date>
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<item>
<title>Solving Constraint Satisfaction Puzzles with Constraint Programming</title>
<link>http://hdl.handle.net/10226/474</link>
<description>Solving Constraint Satisfaction Puzzles with Constraint Programming
Crawford, Broderick; Castro, Carlos; Monfroy, Eric; Rodriguez, Nibaldo
Constraint Programming (CP) is a powerful paradigm for solving&#13;
Combinatorial Problems (generally issued from Decision Making). In CP,&#13;
Enumeration Strategies are crucial for resolution performances. In this work,&#13;
we model the known benchmark problems Latin Square, Magic Square and&#13;
Sudoku as a Constraint Satisfaction Problems. We solve them with Constraint&#13;
Programming comparing the performance of di erent Variable and Value&#13;
Selection Heuristics in its Enumeration phase. The platform used was Mozart
</description>
<pubDate>Thu, 17 Sep 2009 19:27:02 GMT</pubDate>
<guid isPermaLink="false">http://hdl.handle.net/10226/474</guid>
<dc:date>2009-09-17T19:27:02Z</dc:date>
</item>
<item>
<title>Actions Combination Method for Reinforcement Learning</title>
<link>http://hdl.handle.net/10226/473</link>
<description>Actions Combination Method for Reinforcement Learning
Karanik, Marcelo J.; Gramajo, Sergio D.
The software agents are programs that can perceive from their environment and they act to reach their design goals.&#13;
In most cases the selected agent architecture determines its behaviour in response to different problem states.&#13;
However, there are some problem domains in which it is desirable that the agent learns a good action execution&#13;
policy by interacting with its environment. This kind of learning is called Reinforcement Learning (RL) and is&#13;
useful in the process control area. Given a problem state, the agent selects the adequate action to do and receives an&#13;
immediate reward. Then it actualizes its estimations about every action and, after a certain period of time, the agent&#13;
learns which the best action to execute is. Most RL algorithms execute simple actions even if two o more can be&#13;
executed. This work involves the use of RL algorithms to find an optimal policy in a gridworld problem and&#13;
proposes a mechanism to combine actions of different types.
</description>
<pubDate>Thu, 17 Sep 2009 19:26:43 GMT</pubDate>
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<dc:date>2009-09-17T19:26:43Z</dc:date>
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