Challenges of Portfolio-based Planning

In the recent years the field of automated planing has significantly advanced and several powerful domain-independent planners have been developed. However, none of these systems clearly outperforms all the others in every known benchmark domain. This observation motivated the idea of configuring and exploiting a portfolio of planners to achieve better performances than any individual planner: some recent planning systems based on this idea obtained significantly good results in experimental analysis and International Planning Competitions. Such results lead us to think that future challenges for the automated planning community will converge on designing different approaches for combining existing planning algorithms. This paper focuses on the challenges and open issues of existing approaches and highlights the possible future evolution of these techniques. In addition the paper introduces algorithm portfolios, reviews existing techniques, and describes the decisions that have to be taken during the configuration

An Automatic Algorithm Selection Approach for Planning

Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, extracted in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings--planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans

Dynamic real-time hierarchical heuristic search for pathfinding.

Movement of Units in Real-Time Strategy (RTS) Games is a non-trivial and challenging task mainly due to three factors which are constraints on CPU and memory usage, dynamicity of the game world, and concurrency. In this paper, we are focusing on finding a novel solution for solving the pathfinding problem in RTS Games for the units which are controlled by the computer. The novel solution combines two AI Planning approaches: Hierarchical Task Network (HTN) and Real-Time Heuristic Search (RHS). In the proposed solution, HTNs are used as a dynamic abstraction of the game map while RHS works as planning engine with interleaving of plan making and action executions. The article provides algorithmic details of the model while the empirical details of the model are obtained by using a real-time strategy game engine called ORTS (Open Real-time Strategy). The implementation of the model and its evaluation methods are in progress however the results of the automatic HTN creation are obtained for a small scale game map

ASCoL: Automated Acquisition of Domain Specific Static Constraints from Plan Traces

Domain-independent planning systems require that domain constraints and invariants are specified as part of the input domain model. In AI Planning, the generated plan is correct provided the constraints of the world in which the agent is operating are satisfied. Specifying operator descriptions by hand for planning domain models that also require domain specific constraints is time consuming, error prone and still a challenge for the AI planning community. The LOCM (Cresswell, McCluskey, and West 2013) system carries out automated generation of the dynamic aspects of a planning domain model from a set of example training plans. We enhance the output domain model of the LOCM system to capture static domain constraints from the same set of input training plans as used by LOCM to learn dynamic aspects of the world. In this paper we propose a new framework ASCoL (Automated Static Constraint Learner), to make constraint acquisition more efficient, by observing a set of training plan traces. Most systems that learn constraints automatically do so by analysing the operators of the planning world. Out proposed system will discover static constraints by analysing plan traces for correlations in the data. To do this an algorithm is in the process of development for graph discovery from the collection of ground action instances used in the input plan traces. The proposed algorithm will analyse the complete set of plan traces, based on a predefined set of constraints, and deduces facts from it. We then augment components of the LOCM generated domain with enriched constraints

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

ASAP: An Automatic Algorithm Selection Approach for Planning

Despite the advances made in the last decade in automated planning, no planner out- performs all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners’ performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a plan- ner can be improved by exploiting additional knowledge, for instance, in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings–planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans

Sexual citizenship in Belfast, Northern Ireland

In this article we examine the contours and construction of sexual citizenship in Belfast, Northern Ireland through in-depth interviews with 30 members of the GLBT community and a discursive analysis of discourses of religion and nationalism. In the first half of the article we outline how sexual citizenship was constructed in the Irish context from the mid-nineteenth century onwards, arguing that a moral conservatism developed as a result of religious reform and the interplay between Catholic and Protestant churches, and the redefining of masculinity and femininity with the rise of nationalism. In the second half of the article, we detail how the Peace Process has offered new opportunities to challenge and destabilise hegemonic discourses of sexual citizenship by transforming legislation and policing, and encouraging inward investment and gentrification

A Tool-Supported Approach to Engineering HTN Planning Models

Our research concerns formal, expressive, object centred languages and tools for use in engineering domains for planning applications. In this paper we extend our recent work on an object-centred language for encoding precondition planning domains to a language called OCLh, designed for HTN planning. Domain encodings for HTN planners are particularly troublesome, because they tend to be used in knowledged-based applications requiring a great deal of ‘domain engineering’, and the abstract operators central to an HTN model do not share the fairly clear declarative semantics of concrete pre- and post condition operators. Central to our approach is the development, in parallel, of the abstract operator set and the hierarchical state specification of the objects that the operators manipulate. In this paper we define and illustrate a transparency property, together with a transparency checking tool, which helps the developer to encode a clear planning model in OCLh. Our encoding of the Translog domain is used as an extended example to illustrate the approach

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 developmen

OCLh: A Sound and Supportive Planning Domain Modelling Language

In this paper we postulate OCLh as a prototype for future planning domain modelling languages which are foundationally sound, but offer features that are attractive and supportive to knowledge engineers. The novel contributions of this paper is that it (a) describes a truth criterion for OCLh and details a proof that the criterion is sufficient for ensuring necessary truth in a partial plan structure (b) evaluates OCLh, illustrating its pragmatic benefits by comparing it with O-Plan’s TF. We show using a real example how OCLh’s structuring devices aid the knowledge engineer in building a model. Finally, the example and comparison with TF identifies further development work to advance OCLh as potential high level research language for modelling operator based planning domains