Abstract:In recent years the field of automated planning 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 perform better than any individual planner: some recent planning systems based on this idea achieved significantly good results in experimental analysis and International Plannin… Show more
“…The declared winner, IBaCoP2, is a portfolio-based planner, a different type of planner to the winner of IPC 2011. Since 2011 portfolio-based planners have undergone significant research and development resulting in new planning features being proposed (see, e.g., (Cenamor et al, 2012;Fawcett et al, 2014)), and different techniques for combining planners into a portfolio being developed (Vallati et al, 2015b). As a result of the work done in the area, portfolio-based solvers are more reliable and effective, in that they can better predict the performance of basic components in order to combine them effectively.…”
Section: Distinguished Performersmentioning
confidence: 99%
“…For detailed descriptions of solvers, the interested reader is referred to Vallati et al . (2014) or to the IPC website 3 .Figure 1The structure of the deterministic part of International Planning Competition (IPC) 2014. Dashes indicate tracks that have been cancelled…”
The International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.
“…The declared winner, IBaCoP2, is a portfolio-based planner, a different type of planner to the winner of IPC 2011. Since 2011 portfolio-based planners have undergone significant research and development resulting in new planning features being proposed (see, e.g., (Cenamor et al, 2012;Fawcett et al, 2014)), and different techniques for combining planners into a portfolio being developed (Vallati et al, 2015b). As a result of the work done in the area, portfolio-based solvers are more reliable and effective, in that they can better predict the performance of basic components in order to combine them effectively.…”
Section: Distinguished Performersmentioning
confidence: 99%
“…For detailed descriptions of solvers, the interested reader is referred to Vallati et al . (2014) or to the IPC website 3 .Figure 1The structure of the deterministic part of International Planning Competition (IPC) 2014. Dashes indicate tracks that have been cancelled…”
The International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.
“…There are planner portfolio configuration systems mainly designed to automatically generate domain-optimized portfolio planners, such as PbP and ASAP [6,7], as well as a range of domain-independent portfolio planners [8]. Among the latter, we can identify two main classes: static portfolios, which run the same schedule of planners on every given problem instance, and portfolios based on per-instance planner schedules.…”
Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning.\ud
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Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation
“…exploiting a mixture of techniques can be fruitful), we also consider different portfolio approaches in order to highlight (relative) strengths and weaknesses of solvers. As testified by experiences in other research areas in artificial intelligence, such as planning [14], SAT [15], and ASP [16], portfolios and algorithm selection techniques [17] are very useful tools for understanding the importance of solvers, evaluate the improvements, and effectively combine solvers for increasing overall performance.…”
In this paper we consider the impact of configuration of abstract argumentation reasoners both when using a single solver and choosing combinations of framework representation-solver options; and also when composing portfolios of algorithms. To exemplify the impact of the framework-solver configuration we consider one of the most configurable solvers, namely ArgSemSAT-runner-up of the last competition on computational models of argumentation (ICCMA-15)-for enumerating preferred extensions. We discuss how to configure the representation of the argumentation framework in the input file and show how this coupled framework-solver configuration can have a remarkable impact on performance. As to the impact of configuring differently structured portfolios of abstract argumentation solvers, we consider the solvers submitted to ICCMA-15, which provided the community with a heterogeneous panorama of approaches for handling abstract argumentation frameworks. A superficial reading of the results of ICCMA-15 is that reduction-based systems (either SAT-based or ASP-based) are always the most efficient. Our investigation, concerning the enumeration of stable and preferred extensions, shows that this is not true in full generality and suggests the areas where the relatively underdeveloped non reduction-based systems should focus more to improve their performance. Moreover, it also highlights that the state-of-the-art solvers are very complementary and can be successfully combined in portfolios.
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