The 2014 International Planning Competition: Progress and Trends

AI 2017: Advances in Artificial Intelligence

Cerutti

Giacomin

2017

Self Cite

Abstract. In the light of the increasing interest in efficient algorithms for solving abstract argumentation problems and the pervasive availability of multicore machines, a natural research issue is to combine existing argumentation solvers into parallel portfolios. In this work, we introduce six methodologies for the automatic configuration of parallel portfolios of argumentation solvers for enumerating the preferred extensions of a given framework. In particular, four methodologies aim at combining solvers in static portfolios, while two methodologies are designed for the dynamic configuration of parallel portfolios. Our empirical results demonstrate that the configuration of parallel portfolios is a fruitful way for exploiting multicore machines, and that the presented approaches outperform the state of the art of parallel argumentation solvers.

Section: Experimental Analysismentioning

confidence: 99%

On the Combination of Argumentation Solvers into Parallel Portfolios

AI 2017: Advances in Artificial Intelligence

Cerutti

Giacomin

2017

Self Cite

“…For solving such problems, we selected Yahsp3 [21], a planner that achieved remarkable results in recent International Planning Competitions (IPCs) [19] -the major competition run by the planning community-and that is able to handle the set of PDDL features required by the encoded domain model. It should be noted that Yahsp3 is not an optimal solver -i.e.…”

Section: A Settingsmentioning

confidence: 99%

“…Applying Automated Planning for decision support in Traffic Accident Management has been considered bÿ Ozbay et al [17], where probabilistic models have been used for planning operations -but not for reacting to actual reported accidents-, and by Shah et al [18], which considered "classical" domain-independent planning. It should be noted that in the work of Shah et al, the traffic accident management domain was mainly investigated as a case study for comparing Knowledge Engineering techniques; one of the resulting domain models was included in the temporal track of the last International Planning Competition [19]. In terms of modelling of real-world scenarios, it was quite simplistic.…”

Section: Introductionmentioning

confidence: 99%

On the exploitation of Automated Planning for efficient decision making in road traffic accident management

Chrpa

2016 IEEE 55th Conference on Decision and Control (CDC)

2016

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Abstract-Automated Planning can be fruitfully exploited as a Decision Support toolkit that, given a specification of available actions (elementary decisions to be taken), an initial situation and goals to be achieved, generates a plan that represents a (partially ordered) sequence of such elementary decisions that once performed the required goals are achieved. Road Traffic Accident Management is a life-critical task that deals with effective planning of emergency response when accidents occur, in order to mitigate negative effects, especially saving human lives that might be in imminent danger.In this paper, we exploit Automated Planning in the Road Traffic Accident Management domain. We specifically focus on providing necessary treatment for victims injured during accidents. This involves coordination of medical teams responsible for providing medical treatment to the victims and fire brigades that are required to release victims trapped in damaged vehicles. An empirical analysis, based in the region of West Yorkshire (UK) with a number of real accidents recently occurred there, shows the suitability of the proposed Automated Planning approach to be used in time-critical conditions, and confirms the effectiveness of the generated plans. We also demonstrated its usefulness as a tool for evaluating the impact of additional resources, in order to provide guidance for future investments.

“…Even more worrying is the fact that some teams were not aware of the existence of KEPS tools. Secondly, the number of participants of ICKEPS is still not very large, especially when compared with the latest edition of the International Planning Competition [36]: this suggests that the planning community underestimates the importance of knowledge engineering, despite its enormous impact on applicability of domain-independent planning in real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

Engineering Knowledge for Automated Planning

McCluskey

Vaquero

Proceedings of the Knowledge Capture Conference

2017

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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 domainindependent 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.