Utilización de metodologías de Inteligencia Artificial y sus aplicaciones en El Salvador

El presente artículo intenta dar una pequeña perspectiva de cómo el uso de las metodologías basadas en Inteligencia Artificial (IA) podrían contribuir en la solución de problemas reales del país: como la eficiencia y eficacia en consultas médicas del Instituto del Seguro Social Salvadoreño (ISSS), toma de decisiones políticas importantes, resolución de juicios legales, evasión de impuestos, aprobación de créditos, optimización de recursos, etc. El documento describe brevemente diferentes técnicas de Inteligencia Artificial (IA) tales como Sistemas Expertos (SE), Razonamiento Basados en Casos (RBC), Redes Neuronales Artificiales (RNA) y Algoritmos Genéticos (AG) entre otras, y menciona en forma sintetizada algunas áreas críticas en las que podrían aplicarse en el país con éxito. El objetivo principal de este artículo es dar a conocer otras alternativas hasta ahora desconocidas por las instituciones del Estado para la resolución de problemas nacionales importantes

An algorithm for the induction Of defeasible logic theories from databases

Defeasible logic is a non-monotonic logic with applications in rule-based domains such as law. To ease the development and improve the accuracy of expert systems based on defeasible logic, it is desirable to automatically induce a theory of the logic from a training set of precedent data. Empirical evidence suggests that minimal theories that describe the training set tend to be more faithful representations of reality. We show via transformation from the hitting set problem that this global minimization problem is intractable, belonging to the class of NP optimisation problems. Given the inherent difficulty of finding the optimal solution, we instead use heuristics and demonstrate that a best-first, greedy, branch and bound algorithm can be used to find good theories in short time. This approach displays significant improvements in both accuracy and theory size as compared to recent work in the area that post-processed the output of an Aprori association rule-mining algorithm, with comparable execution times

Induction of defeasible logic theories in the legal domain

The market for intelligent legal information systems remains relatively untapped and while this might be interpreted as an indication that it is simply impossible to produce a system that satisfies the needs of the legal community, an analysis of previous attempts at producing such systems reveals a common set of deficiencies that in-part explain why there have been no overwhelming successes to date. Defeasible logic, a logic with proven successes at representing legal knowledge, seems to overcome many of these deficiencies and is a promising approach to representing legal knowledge. Unfortunately, an immediate application of technology to the challenges in this domain is an expensive and computationally intractable problem. So, in light of the benefits, we seek to find a practical algorithm that uses heuristics to discover an approximate solution. As an outcome of this work, we have developed an algorithm that integrates defeasible logic into a decision support system by automatically deriving its knowledge from databases of precedents. Experiments with the new algorithm are very promising - delivering results comparable to and exceeding other approaches

Arguments, rules and cases in law: Resources for aligning learning and reasoning in structured domains

This paper provides a formal description of two legal domains. In addition, we describe the generation of various artificial datasets from these domains and explain the use of these datasets in previous experiments aligning learning and reasoning. These resources are made available for the further investigation of connections between arguments, cases and rules. The datasets are publicly available at https://github.com/CorSteging/LegalResource