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The 2014 International Planning Competition: Progress and Trends
The 2014 International Planning Competition (IPC-2014) was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part, the learning part (IPCL), and the probabilistic part ( IPPC). Expand
ArgSemSAT: Solving Argumentation Problems Using SAT
In this paper we describe the system ArgSemSAT which includes algorithms which we proved to overcome current state-of-the-art performances in enumerating preferred extensions.
Computing Preferred Extensions in Abstract Argumentation: A SAT-Based Approach
A novel SAT-based approach for the computation of extensions in abstract argumentation, with focus on preferred semantics, and an empirical evaluation of its performances. Expand
An SCC Recursive Meta-Algorithm for Computing Preferred Labellings in Abstract Argumentation
This paper presents a meta-algorithm for the computation of preferred labellings, based on the general recursive schema for argumentation semantics called SCC-Recursiveness, and devise for this purpose a generalization of a SAT-based algorithm, and provides an empirical investigation to show the significant improvement of performances obtained by exploiting the S CC-recursive schema. Expand
An Automatically Configurable Portfolio-based Planner with Macro-actions: PbP
P is a multi-planner which automatically configures a portfolio of planners by computing some sets of macro-actions for every planner in the portfolio, selecting a promising combination of planners in the portfolios and relative useful macro- actions, and defining some running time slots for their round-robin scheduling during planning. Expand
Summary Report of The First International Competition on Computational Models of Argumentation
Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers. Expand
Improved Features for Runtime Prediction of Domain-Independent Planners
This work proposes a new, extensive set of instance features for planning, and investigates its effectiveness across a range of model families, and concludes that its models predict runtime much more accurately than the previous state of the art. Expand
Embedding Automated Planning within Urban Traffic Management Operations
The resulting system which works by sourcing and semantically enriching urban traffic data, and uses the derived knowledge as input to an automated planning component to generate light signal control strategies in real time is described. Expand
On the Effective Configuration of Planning Domain Models
This paper introduces a fully automated method for this configuration task, and shows that this process (which can, in principle, be combined with other forms of reformulation and configuration) can have a remarkable impact on performance across planners. Expand
MUM: A Technique for Maximising the Utility of Macro-operators by Constrained Generation and Use
A new learning technique for synthesising macros from training example plans in order to improve the speed and coverage of domain independent automated planning engines and maximising the utility of used macros is introduced. Expand