Ron M. Simpson

Planning domain definition using GIPO

In this paper an object-centric perspective on planning domain definition is presented along with an overview of GIPO (graphical interface for planning with objects), a supporting tools environment. It is argued that the object-centric view assists the domain developer in conceptualizing the domain’s structure, and we show how GIPO enables the developer to capture that conceptualization at an appropriate and matching conceptual level. GIPO is an experimental environment which provides a platform for exploring and demonstrating the range and scope of tools required to support the knowledge engineering aspects of creating and validating planning systems, both for classical pre-condition planning and hierarchical planning. GIPO embodies the object-centric view, leading to a range of benefits typically associated with object-oriented methods in other fields of software engineering such as highly visual development methods, code reuse and efficient, reliable development.

Case-Bases Incorporating Scheduling Constraint Dimensions - Experiences in Nurse Rostering

This paper looks at work done on a case-based workforce scheduling application. Generalised patterns of workforce allocation are used to build up a schedule that is then adjusted to remove any problems. Because some constraint elements are incorporated in the case-base, the global problem search space is reduced. The case-base can be maintained either automatically, by generalisation of solutions or by theoretical analysis of case efficiency, or manually, by storage of generalised patterns of allocation preference. In line with the background cognitive theory behind case-based reasoning, the methods of arrival at solutions are very similar to the methods used by manual schedulers.

Knowledge formulation for AI planning

In this paper we present an overview of the principle components of GIPO, an environment to support knowledge acquisition for AI Planning. GIPO assists in the knowledge formulation of planning domains, and in prototyping planning problems within these domains. GIPO features mixed-initiative components such as generic type composition, an operator induction facility, and various plan animation and validation tools. We outline the basis of the main tools, and show how an engineer might use them to formulate a domain model. Throughout the paper we illustrate the formulation process using the Hiking Domain.

Knowledge Representation in Planning: A PDDL to OCLh Translation

Recent successful applications of AI planning technology have highlighted the knowledge engineering of planning domain models as an important research area. We describe an implemented translation algorithm between two languages used in planning representation: PDDL, a language used for communication of example domains between research groups, and OCLh, a language developed specifically for planning domain modelling. The algorithm is being used as part of OCLhs tool support to import models expressed in PDDL to OCLhs environment. Here we outline the translation algorithm, and discuss the issues that it uncovers. Although the tool performs reasonably well when its output is measured against hand-crafted OCLh, it results in only partially specified models. Analyis of the translation results shows that this is because many natural assumptions about domains are not captured in the PDDL encodings.

The use of an integrated tool to support teaching and learning in artificial Intelligence

Abstract Teaching of knowledge-intensive AI is particularly hard as the process of how knowledge is acquired is difficult to grasp without practical experience. Acquiring and using knowledge about actions, events, processes is especially difficult because of the temporal nature of the subject matter. In this paper we report on a tool called GIPO that has been used for teaching AI students the areas of knowledge acquisition, knowledge engineering, automated planning and machine learning. We give a short walkthrough of some of GIPO’s functions, indicating some of the learning opportunities offered. We then compare GIPO with other interfaces used in the computing curriculum. We argue that using a high level integrated tool such as GIPO for supporting teaching and learning improves the students’ learning experience, and helps integrate the theory and practice in a range of AI and related subject areas.

A Tool Supported Structured Method for Planning Domain Acquisition

Knowledge engineering in AI planning is the process that deals with the acquisition, validation and maintenance of planning domain models, and the selection and optimisation of appropriate planning machinery to work on them. Our aim is to research and develop rigorous methods for the acquisition, maintenance and validation of planning domain models. We aim to provide a tools environment suitable for use by domain experts in addition to experts in the field of AI planning. In this paper we describe such a method and illustrate it with screen-shots taken from an implemented Graphical Interface for Planning with Objects system called GIPO. The GIPO tools environment has been built to support an object centred approach to planning domain modelling. The principal innovation we present in this paper is a process of specifying domain operators that abstracts away much of the technical detail traditionally required in their specification. Such innovations we believe could ultimately open up the possibility of bringing planning technology to a wider public.

Selecting and Comparing Multiple Cases to Maximise Result Quality after Adaptation in Case-Based Adaptive Scheduling

Recent Case-Based Reasoning research has begun to refocus attention on the problem of automatic adaptation of the retrieved case to give a fuller solution to the new problem. Such work has highlighted problems with the usefulness of similarity assessment of cases where adaptation is involved. As a response to this, methods of case selection are evolving that take adaptation into account. This current work looks more closely at the relationship between selection and adaptation. It considers experimental evidence considering adaptation of multiple cases for one problem. It argues that selection of the best case after adaptation will often make more efficient use of case knowledge than any attempt to pre-select a single case for adaptation.

Dynamic OWL Ontology Design Using UML and BPMN

Ontology design is a crucial task for the Semantic Web. In the literature, methodologies have been proposed to develop ontologies, however the phase between knowledge gathering and knowledge coding remains challenging. In this paper, we propose a dynamic ontology design based on dynamic design notations for a systematic identification of the relations between domain concepts. For this purpose, we propose the use of the Unified Modeling Language (UML) and the Business Process Modeling Notation (BPMN), and the mapping of the related dynamic notations to the ontology domain. Our approach has been successfully validated in a study case of an ontology with a publication repository domain.