PLTOOL: A knowledge engineering tool for planning and learning

Abstract

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 domaindependent 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.

From Unstructured Web Knowledge to Plan Descriptions

“…PAA has been developed in Java using the version 1.6.0.11 of the Sun Java Development Kit. NetBeans 6.5.1 11 has been used as IDE. For the experimental phase, a GUI and a Glassfish WebService integrating the architecture have been deployed.…”

“…So, there is a need for techniques and tools that either allow an interaction with domain experts in their usual language, or automatically (or semi-automatically) acquire knowledge from current sources of plans and actions described in semi-structured or unstructured formats. In the first case, there has been some work on knowledge acquisition tools for planning as GIPO [20], techniques for domain models acquisition [13,22,24], or tools that integrate planning and machine learning techniques [26,11]. In the second case, there has been very little work on building plans from human generated plans or actions models described in semi-structured or unstructured formats, as filling natural language descriptions on templates.…”

Efficient approaches for multi-agent planning

“…Given a plan π, a macro-operator is defined as a new action m π that has as preconditions those conditions of actions in the plan that are not achieved by previous actions in the plan, and whose effects are the ones that are added or deleted by the execution in sequence of the actions in π. They were defined in the strips planner and have been successfully used as a learning technique [30,31]. The idea of sharing macro-operators rather than individual actions is that details on the underlying plan are missing and thus cannot be inferred by the next agents.…”