Un enfoque para la generación de plan corpus con un algoritmo de planning

Obtener un plan corpus a partir del cual aprender una biblioteca de planes que sirva de base de conocimiento para un sistema de reconocimiento de planes es una tarea difícil. En este trabajo se presenta un enfoque basado en planning para la generación automática de secuencias de acciones que llevan al cumplimiento de diferentes objetivos. El método propuesto permite adicionalmente la especificación de preferencias que se utilizan a la hora de elegir qué acción seguir en un punto determinado del plan de manera tal de poder modelar fácilmente diferentes perfiles de usuarios que utilicen el sistema.Sociedad Argentina de Informática e Investigación Operativ

A mixed-initiative approach to computer aided process planning

Several approaches have been proposed in order to develop intelligent applications on Computer Aided Process Planning (CAPP) domain. These approaches range from historic designs storage for later recovery, to generative synthesis of process plans. Although these approaches present advantages over traditional methods, they have several drawbacks derived specially from the under and over-automation of the decision processes. In this paper a mixed-initiative model for CAPP systems is proposed, that integrates plan recognition of user’s intentions, with planning techniques in the context of artificial intelligence, in order to synthesize new designs that fulfill process planner’s inferred intentions, thus improving the usability and usefulness of such intelligent assistants.Red de Universidades con Carreras en Informática (RedUNCI

Supporting Interleaved Plans in Learning Hierarchical Plan Libraries for Plan Recognition

Most of the available plan recognition techniques are based on the use of a plan library in order to infer user's intentions and/or strategies. Until some years ago, plan libraries were completely hand coded by human experts, which is an expensive, error prone and slow process. Besides, plan recognition systems with hand-coded plan libraries are not easily portable to new domains, and the creation of plan libraries require not only a domain expert, but also a knowledge representation expert. These are the main reasons why the problem of automatic generation of plan libraries for plan recognition, has gained much importance in recent years. Even when there is considerable work related to the plan recognition process itself, less work has been done on the generation of such plan libraries. In this paper, we present an algorithm for learning hierarchical plan libraries from action sequences, based on a few simple assumptions and with little given domain knowledge, and we provide a novel mechanism for supporting interleaved plans in the input example cases