Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan

Aprendizaje de conocimiento de control para planificación de tareas

Esta tesis doctoral se centra en dos disciplinas de la Inteligencia Artificial: la planificación de tareas y el aprendizaje automático. La planificación en IA desarrolla sistemas de computación que resuelvan problemas cuya solución consista de un conjunto de acciones, total o parcialmente ordenadas, denominado plan que, mediante su ejecución, consiguen pasar de una situación inicial a otra situación en que se hacen ciertas una serie de metas o fines perseguidos .Debido al enorme tamaño del espacio de búsqueda que hay que explorar, se considera un problema PSPACE-complete [Vil Ander,1991]. Normalmente es necesario definir algún tipo de conocimiento o heurıstica que permita obtener eficientemente el plan. Por otro lado, se dice de un programa de ordenador que aprende apartir de la experiencia E, con respecto a alguna clase de area T y una medida de rendimiento P, si su rendimiento en la tarea T, medido mediante P, mejora con la experiencia E[Mitchell,1997] El objetivo de esta tesis es el desarrollo de sistemas de aprendizaje automático aplicables a planificación, para extraer el conocimiento recontrol (o heurısticas) adecuado que incremente la eficiencia (disminuya el tiempo y la memoria consumidos por el ordenador) en resoluciones de nuevos problemas. También se busca definir una metodología de aprendizaje adaptable a distintos paradigmas de planificación capaz de obtener heurısticas. Por ultimo, se estudia la transferencia de dicho conocimiento, primero entre dos planificadores distintos; es decir, se extrae el conocimiento de una técnica de planificación específica, utilizando la metodología diseñada, para aplicarlo en otra diferente; y segundo, a los sistemas de aprendizaje :como le afecta al rendimiento del sistema de aprendizaje la utilización de un conocimiento previo proveniente de una fuente externa. Para poder realizar estas transferencias se necesita haber definido previamente un lenguaje de representación del conocimiento de control__________________________________________________ The research presented in this dissertation focuses on two topics in Artificial Intelligence: planning and machine learning. The aim of classical planning is to find plans to solve problems. A problem is made of a starting situation and a set of goals. Given a set of possible actions, a planner must find a sequence of actions (a plan) that, starting from the initial state, fulfills all goals. Planning has a high computational complexity. In fact, it is PSPACE-complete [Bylander, 1991]. Usually, in order to make the search process more efficient, some heuristics are needed. On the other hand, a computer program is said to learn from experience E, with respect to some task T and a performance measure P, if its performance on T according to P, improves with experience E [Mitchell, 1997]. This thesis aims to show that machine learning methods can improve efficiency (time and memory) in modern planners, like graphplan-based planning or agentbased hierarchical partial-order planners. From this experience, a general methodology is developed to apply machine learning to different planning paradigms. Finally, this thesis explores the idea of transferring heuristics learned in one planner to a different planner. In particular, we transfer heuristics from a graphplan-based planner to a bidirectional on

Generative planner for hybrid systems with temporally extended goals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 230-237).Most unmanned missions in space and undersea are commanded by a "script" that specifies a sequence of discrete commands and continuous actions. Currently such scripts are mostly hand-generated by human operators. This introduces inefficiency, puts a significant cognitive burden on the engineers, and prevents re-planning in response to environment disturbances or plan execution failure. For discrete systems, the field of autonomy has elevated the level of commanding by developing goal-directed systems, to which human operators specify a series of temporally extended goals to be accomplished, and the goal-directed systems automatically output the correct, executable command sequences. Increasingly, the control of autonomous systems involves performing actions with a mix of discrete and continuous effects. For example, a typical autonomous underwater vehicle (AUV) mission involves discrete actions, like get GPS and take sample, and continuous actions, like descend and ascend, which are influenced by the dynamical model of the vehicle. A hybrid planner generates a sequence of discrete and continuous actions that achieve the mission goals. In this thesis, I present a novel approach to solve the generative planning problem for temporally extended goals for hybrid systems, involving both continuous and discrete actions. The planner, Kongming, incorporates two innovations. First, it employs a compact representation of all hybrid plans, called a Hybrid Flow Graph, which combines the strengths of a Planning Graph for discrete actions and Flow Tubes for continuous actions. Second, it engages novel reformulation schemes to handle temporally flexible actions and temporally extended goals. I have successfully demonstrated controlling an AUV in the Atlantic ocean using mission scripts solely generated by Kongming. I have also empirically evaluated Kongming on various real-world scenarios in the underwater domain and the air vehicle domain, and found it successfully and efficiently generates valid and optimal plans.by Hui X. Li.Ph.D

Kongming: A Generative Planner for Hybrid Systems with Temporally Extended Goals

PhD thesisMost unmanned missions in space and undersea are commanded by a "script" that specifies a sequence of discrete commands and continuous actions. Currently such scripts are mostly hand-generated by human operators. This introduces inefficiency, puts a significant cognitive burden on the engineers, and prevents re-planning in response to environment disturbances or plan execution failure. For discrete systems, the field of autonomy has elevated the level of commanding by developing goal-directed systems, to which human operators specify a series of temporally extended goals to be accomplished, and the goal-directed systems automatically output the correct, executable command sequences. Increasingly, the control of autonomous systems involves performing actions with a mix of discrete and continuous effects. For example, a typical autonomous underwater vehicle (AUV) mission involves discrete actions, like get GPS and take sample, and continuous actions, like descend and ascend, which are influenced by the dynamical model of the vehicle. A hybrid planner generates a sequence of discrete and continuous actions that achieve the mission goals. In this thesis, I present a novel approach to solve the generative planning problem for temporally extended goals for hybrid systems, involving both continuous and discrete actions. The planner, Kongming, incorporates two innovations. First, it employs a compact representation of all hybrid plans, called a Hybrid Flow Graph, which combines the strengths of a Planning Graph for discrete actions and Flow Tubes for continuous actions. Second, it engages novel reformulation schemes to handle temporally flexible actions and temporally extended goals. I have successfully demonstrated controlling an AUV in the Atlantic ocean using mission scripts solely generated by Kongming. I have also empirically evaluated Kongming on various real-world scenarios in the underwater domain and the air vehicle domain, and found it successfully and efficiently generates valid and optimal plans.Funded by the Boeing Company under contract MIT-BA-GTA-