What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

Vallati, Mauro; Chrpa, Lukáš; McCluskey, T.L.

doi:10.1017/s0269888918000012

Cited by 24 publications

(24 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To analyse the results we considered two metrics: IPC (the higher the better) and PAR10 (the lower the better). IPC is the International Planning Competition score, as defined for the Agile track of the 2014 edition of the competition [34,35], and represents a relative performance measure among a set of compared solvers: for a solver C and a problem p, ScorepC, pq is 0 if p is unsolved, and 1{p1`log 10 pT p pCq{Tp qq otherwise (where T p pC) is the amount of time required by C to solve the problem p, and Tp is the minimum amount of time required by any compared system). The IPC score on a set of instances is given by the sum of the scores achieved on each considered instance.…”

Section: Experimental Settingsmentioning

confidence: 99%

How we designed winning algorithms for abstract argumentation and which insight we attained

Cerutti

Giacomin

Vallati

2019

Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

In this paper we illustrate the design choices that led to the development of ArgSemSAT, the winner of the preferred semantics track at the 2017 International Competition on Computational Models of Arguments (ICCMA 2017), a biennial contest on problems associated to the Dung's model of abstract argumentation frameworks, widely recognised as a fundamental reference in computational argumentation. The algorithms of ArgSemSAT are based on multiple calls to a SAT solver to compute complete labellings, and on encoding constraints to drive the search towards the solution of decision and enumeration problems. In this paper we focus on preferred semantics (and incidentally stable as well), one of the most popular and complex semantics for identifying acceptable arguments. We discuss our design methodology that includes a systematic exploration and empirical evaluation of labelling encodings, algorithmic variations and SAT solver choices. In designing the successful ArgSemSAT, we discover that: (1) there is a labelling encoding that appears to be universally better than other, logically equivalent ones; (2) composition of different techniques such as AllSAT and enumerating stable extensions when searching for preferred semantics brings advantages; (3) injecting domain specific knowledge in the algorithm design can lead to significant improvements.

show abstract

Section: Experimental Settingsmentioning

confidence: 99%

How we designed winning algorithms for abstract argumentation and which insight we attained

Cerutti

Giacomin

Vallati

2019

Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the Optimal track, the Tidybot domain has been used in place of Thoughtful. For each track, 20 instances per domain were selected following a specifically-designed protocol [18].…”

Section: The International Planning Competitionmentioning

confidence: 99%

“…For more information about the competition, including complete results, source code of planning systems, and domain models, the interested reader is referred to the analysis of the IPC 2014 results [18], and to the official competition website. 2 Detailed descriptions of the planning systems can be found in the IPC 2014 booklet [19].…”

Section: The International Planning Competitionmentioning

confidence: 99%

Performance robustness of AI planners in the 2014 International Planning Competition

Bocchese

Fawcett

Vallati

et al. 2018

AIC

Self Cite

View full text Add to dashboard Cite

Solver competitions have been used in many areas of AI to assess the current state of the art and guide future research and development. AI planning is no exception, and the International Planning Competition (IPC) has been frequently run for nearly two decades. Due to the organisational and computational burden involved in running these competitions, solvers are generally compared using a single homogeneous hardware and software environment for all competitors. To what extent does the specific choice of hardware and software environment have an effect on solver performance, and is that effect distributed equally across the competing solvers? In this work, we use the competing planners and benchmark instance sets from the 2014 IPC to investigate these two questions. We recreate the 2014 IPC Optimal and Agile tracks on two distinct hardware environments and eight distinct software environments. We show that solver performance varies significantly based on the hardware and software environment, and that this variation is not equal for all planners. Furthermore, the observed variation is sufficient to change the competition rankings, including the top-ranked planners for some tracks.

show abstract

“…Influencing AI systems, as reported here, assumes a compatibility of timescales between AI computations, user perception of solution progression, and time constraints of NF input (response time, signal stability, and duration of an epoch). Despite progress made in AI techniques, typical search, planning, and optimisation problems still often require minutes of intensive computations to reach a result, as illustrated by standard benchmarks such as in the international planning competitions [ 62 ], where a cut-off time of 1800 s is introduced [ 63 , 64 ]. These timescales are much more representative of the target applications for our approach than examples such as the 8-puzzle used for proof-of-concept, which tend to be solvable in a few seconds.…”

Section: A Motivational Model Of Ai Controlmentioning

confidence: 99%

A Motivational Model of BCI-Controlled Heuristic Search

Cavazza¹

2018

Brain Sciences

View full text Add to dashboard Cite

Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user’s disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems.

show abstract

What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

Cited by 24 publications

References 39 publications

How we designed winning algorithms for abstract argumentation and which insight we attained

How we designed winning algorithms for abstract argumentation and which insight we attained

Performance robustness of AI planners in the 2014 International Planning Competition

A Motivational Model of BCI-Controlled Heuristic Search

Contact Info

Product

Resources

About