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

OCL Plus:Processes and Events in Object-Centred Planning

An important area in AI Planning is the expressiveness of planning domain specification languages such as PDDL, and their aptitude for modelling real applications. This paper presents OCLplus, an extension of a hierarchical object centred planning domain definition language, intended to support the representation of domains with continuous change. The main extension in OCLplus provides the capability of interconnection between the planners and the changes that are caused by other objects of the world. To this extent, the concept of event and process are introduced in the Hierarchical Task Network (HTN), object centred planning framework in which a process is responsible for either continuous or discrete changes, and an event is triggered if its precondition is met. We evaluate the use of OCLplus and compare it with a similar language, PDDL+

Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain

Formulating knowledge for use in AI Planning engines is currently something 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. This paper seeks to investigate this process using as a case study a road traffic accident management domain. Managing road accidents requires systematic, sound planning and coordination of resources to improve outcomes for accident victims. We have derived a set of requirements in consultation with stakeholders for the resource coordination part of managing accidents. We evaluate two separate knowledge engineering strategies for encoding the resulting planning domain from the set of requirements: (a) the traditional method of PDDL experts and text editor, and (b) a leading planning GUI with built in UML modelling tools. These strategies are evaluated using process and product metrics, where the domain model (the product) was tested extensively with a range of planning engines. 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

An Investigation into Using Object Constraints to Synthesize Planning Domain

This thesis work concerns the area of automated acquisition of planning domain models from one or more examples of plans within the domain under study. It assumes that an adequate domain model for a domain can be composed of objects arranged in collections called object sorts. Recently, two systems have had success in using this underlying assumption: the Opmaker2 system (McCluskey et al. 2009), and the LOCM system (Cresswell, McCluskey, and West 2009). The former requires only one solution plan as input, as long as it contains at least one instance of each operator schema to be synthesized. It does require a partial domain model as well as the example plan, and the initial and goal states of the plan. In contrast LOCM requires no background information, but requires many instances of plans before it can synthesize domain models. Our aim is to build on these systems, and establish an experimental and theoretical basis for using object - centred assumptions to underlie the automated acquisition of planning domain models

A study of synthesizing artificial intelligence (AI) planning domain models by using object constraints.

This paper concerns the area of automated acquisition of planning domain models from one or more examples of plans within the domain under study. It assumes that an adequate domain model for a domain can be composed of objects arranged in collections called object sorts. Recently, two systems have had success in using this underlying assumption: the Opmaker2 system (McCluskey et al. 2009), and the LOCM system (Cresswell, McCluskey, and West 2009). The former requires only one solution plan as input, as long as it contains at least one instance of each operator schema to be synthesized. It does require a partial domain model as well as the example plan, and the initial and goal states of the plan. In contrast LOCM requires no background information, but requires many instances of plans before it can synthesize domain models. Our aim is to build on these systems, and establish an experimental and theoretical basis for using object - centred assumptions to underlie the automated acquisition of planning domain models

Towards Application of Automated Planning in Urban Traffic Control

Advanced urban traffic control systems are often based on feed-back algorithms. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. Therefore, we need self-managing systems that can plan and act effectively in order to restore an unexpected road traffic situations into the normal order. A significant step towards this is exploiting Automated Planning techniques which can reason about unforeseen situations in the road network and come up with plans (sequences of actions) achieving a desired traffic situation. In this paper, we introduce the problem of self-management of a road traffic network as a temporal planning problem in order to effectively navigate cars throughout a road network. We demonstrate the feasibility of such a concept and discuss our preliminary evaluation in order to identify strengths and weaknesses of our approach and point to some promising directions of future research

Modelling Road Traffic Incident Management Problems for Automated Planning

This paper is concerned with the application of automated planning in assisting the decision making and logistics in the area of Road Traffic Incidents. The characteristics of this area are that goals must be posed and plans must be output in real time. The domain is complex, with road topology, information distribution, traffic ows, driver behaviour, and highway management controls all potential factors. The representation and encoding of such domain knowledge, of possible actions and plans, and of potential tasks for the road traffic accident scenario is thus a crucial but difficult issue. The goal of this paper is to explore the potential of automated planning with hierarchical object-centred domain models in the application of Road Traffic Incident Management(RTIM)

Le chien dans la rue aux XVIIe et XVIIIe siècles : entre utilité et nuisance. Le cas des villes du sud de l'actuelle Belgique

Automated planning is a prominent Artificial Intelligence challenge, as well as being a common capability requirement for intelligent autonomous agents. A critical aspect of what is called domain-independent planning, is the application knowledge that must be added to the planner to create a complete planning application. This is made explicit in (i) a domain model, which is a formal representation of the persistent domain knowledge, and (ii) an associated problem instance, containing the details of the particular problem to be solved. Both these components are used by automated planning engines for reasoning, in order to synthesize a solution plan. Formulating knowledge for use in planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers significantly influence the quality of the resulting planning application. On top of that, a notion of quality of the knowledge captured within a domain model is missing; it is therefore hard to provide useful guidelines to knowledge engineers. This paper raises some issues relating to the engineering of application knowledge for automated planning, focussing on the domain model. It uses the idea of a domain model as a formal specification of a domain, and considers what it means to measure the quality of such a specification. To do this it proposes definitions of the attributes of a domain model and its encoding language, which are needed by the automated planning community in order to improve tools for supporting the engineering of planning knowledge, and to advance toward a shared and inclusive definition of quality of domain models