PLTOOL: a knowledge engineering tool for planning and learning

Artificial intelligence (AI) planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will transform some initial state into some desirable end-state. There is a long tradition of work in AI for developing planners that make use of heuristics that are shown to improve their performance in many real world and artificial domains. The developers of planners have chosen between two extremes when defining those heuristics. The domain-independent planners use domain-independent heuristics, which exploit information only from the ‘syntactic’ structure of the problem space and of the search tree. Therefore, they do not need any ‘semantic’ information from a given domain in order to guide the search. From a knowledge engineering (KE) perspective, the planners that use this type of heuristics have the advantage that the users of this technology need only focus on defining the domain theory and not on defining how to make the planner efficient (how to obtain ‘good’ solutions with the minimal computational resources). However, the domain-dependent planners require users to manually represent knowledge not only about the domain theory, but also about how to make the planner efficient. This approach has the advantage of using either better domain-theory formulations or using domain knowledge for defining the heuristics, thus potentially making them more efficient. However, the efficiency of these domain-dependent planners strongly relies on the KE and planning expertise of the user. When the user is an expert on these two types of knowledge, domain-dependent planners clearly outperform domain-independent planners in terms of number of solved problems and quality of solutions. Machine-learning (ML) techniques applied to solve the planning problems have focused on providing middle-ground solutions as compared to the aforementioned two extremes. Here, the user first defines a domain theory, and then executes the ML techniques that automatically modify or generate new knowledge with respect to both the domain theory and the heuristics. In this paper, we present our work on building a tool, PLTOOL (planning and learning tool), to help users interact with a set of ML techniques and planners. The goal is to provide a KE framework for mixed-initiative generation of efficient and good planning knowledge.This work has been partially supported by the Spanish MCyT project TIC2002-04146-C05-05, MEC project TIN2005-08945-C06-05 and regional CAM-UC3M project UC3M-INF-05-016.Publicad

Combining weak learning heuristics in general problem solvers

This paper is concerned with state space problem solvers that achieve generality by learning strong heuristics through experience in a particular domain. We specifically consider two ways of learning by analysing past solutions that can improve future problem solving: creating macros and the chunks. A method of learning search heuristics is specified which is related to 'chunking' but which complements the use of macros within a goal directed system. An example of the creation and combined use of macros and chunks, taken from an implemented system, is described

Technological roadmap on AI planning and scheduling

At the beginning of the new century, Information Technologies had become basic and indispensable constituents of the production and preparation processes for all kinds of goods and services and with that are largely influencing both the working and private life of nearly every citizen. This development will continue and even further grow with the continually increasing use of the Internet in production, business, science, education, and everyday societal and private undertaking. Recent years have shown, however, that a dramatic enhancement of software capabilities is required, when aiming to continuously provide advanced and competitive products and services in all these fast developing sectors. It includes the development of intelligent systems – systems that are more autonomous, flexible, and robust than today’s conventional software. Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has been developed and matured over the last three decades and has successfully been employed for a variety of applications in commerce, industry, education, medicine, public transport, defense, and government. This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning and Scheduling. It identifies the most important research, development, and technology transfer efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in the short-, medium- and longer-term future. The roadmap has been developed under the regime of PLANET – the European Network of Excellence in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating framework for research, development, and technology transfer in the field of Intelligent Planning and Scheduling in Europe. A large number of people have contributed to this document including the members of PLANET non- European international experts, and a number of independent expert peer reviewers. All of them are acknowledged in a separate section of this document. Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing along the directions pointed out in this roadmap will enable a new generation of intelligent application systems in a wide variety of industrial, commercial, public, and private sectors