Knowledge engineering techniques for automated planning

Formulating knowledge for use in AI Planning engines is currently some-thing of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. Also, there is little published research to inform engineers on which method and tools to use in order to effectively engineer a new planning domain model. This is of growing importance, as domain independent planning engines are now being used in a wide range of applications, with the consequence that op-erational problem encodings and domain models have to be developed in a standard language. In particular, at the difficult stage of domain knowledge formulation, changing a statement of the requirements into something for-mal - a PDDL domain model - is still somewhat of an ad hoc process, usually conducted by a team of AI experts using text editors. On the other hand, the use of tools such as itSIMPLE or GIPO, with which experts gen-erate a high level diagrammatic description and automatically generate the domain model, have not yet been proven to be more effective than hand coding. The major contribution of this thesis is the evaluation of knowledge en-gineering tools and techniques involved in the formulation of knowledge. To support this, we introduce and encode a new planning domain called Road Traffic Accidents (RTA), and discuss a set of requirements that we have derived, in consultation with stakeholders and analysis of accident management manuals, for the planning part of the management task. We then use and evaluate three separate strategies for knowledge formulation, encoding domain models from a textual, structural description of require-ments using (i) the traditional method of a PDDL expert and text editor (ii) a leading planning GUI with built in UML modelling tools (iii) an object-based notation inspired by formal methods. We evaluate these three ap-proaches using process and product metrics. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning. In addition, we discuss a range of state-of-the-art modelling tools to find the types of features that the knowledge engineering tools possess. These features have also been used for evaluating the methods used. We bench-mark our evaluation approach by comparing it with the method used in the previous International Competition for Knowledge Engineering for Plan-ning & Scheduling (ICKEPS). We conclude by providing a set of guide-lines for building future knowledge engineering tools

What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed

Representing the Process of Machine Tool Calibration in First-order Logic

Machine tool calibration requires a wide range of measurement techniques that can be carried out in many different sequences. Planning a machine tool calibration is typically performed by a subject expert with a great understanding of International standards and industrial best-practice guides. However, it is often the case that the planned sequence of measurements is not the optimal. Therefore, in an attempt to improve the process, intelligent computing methods can be designed for plan suggestion. As a starting point, this paper presents a way of converting expert knowledge into first-order logic that can be expressed in the PROLOG language. It then shows how queries can be executed against the logic to construct a knowledge-base of all the different measurements that can be performed during machine tool calibration

EmergencyGrid:Planning in Convergence Environments

Government agencies are often responsible for event handling, planning, coordination, and status reporting during emergency response in natural disaster events such as floods, tsunamis and earthquakes. Across such a range of emergency response scenarios, there is a common set of requirements that distributed intelligent computer systems generally address. To support the implementation of these requirements, some researchers are proposing the creation of grids, where final interface and processing nodes perform joint work supported by a network infrastructure. The aim of this project is to extend the concepts of emergency response grids, using a convergence scenario between web and other computational platforms. Our initial work focuses on the Interactive Digital TV platform, where we intend to transform individual TV devices into active final nodes, using a hierarchical planning structure. We describe the architecture of this approach and an initial prototype specification that is being developed to validate some concepts and illustrate the advantages of this convergence planning environment