Mon 17 Jan 2022 13:30 - 13:55 at Directors - Answer Set Programming Chair(s): Martin Gebser

Knowledge discovery techniques had important impact in several relevant application domains. Among the most important knowledge discovery tasks is outlier detection, that is the task of identifying anomalous individuals in a given population. This task is very demanding from the computational complexity theory point of view, being located in the second level of the polynomial hierarchy.

Angiulli et al. in 2007 proposed to employ Answer Set Programming (ASP) to compute outliers. Their solution is based on the saturation technique and, as a consequence, it is very hard to evaluate by ASP systems. In this paper we resort to Quantified Answer Set Programming (QASP) to provide a more declarative, compact and efficient modeling of the outlier detection problem. An experiment on synthetic benchmarks proves that our QASP-based solution can handle databases that are three order of magnitude larger than the ASP-based one proposed by Angiulli et al.

Mon 17 Jan

Displayed time zone: Eastern Time (US & Canada) change

13:30 - 14:45
Answer Set ProgrammingPADL at Directors
Chair(s): Martin Gebser University of Klagenfurt, Austria

Remote session chair

13:30
25m
Talk
Modelling the Outlier Detection Problem in QASPRemote
PADL
Pierpaolo Bellusci University of Calabria, Giuseppe Mazzotta University of Calabria, Fracesco Ricca University of Calabria
13:55
25m
Talk
Multi-Agent Pick and Delivery with Capacities: Action Planning vs Path FindingRemote
PADL
Nima Tajelipirbazari TED University, Çağrı Uluç Yıldırımoğlu Sabanci University, Orkunt Sabuncu TED University, Ali Can Arıcı Ekol Logistics, İdil Helin Özen Ekol Logistics, Volkan Patoğlu Sabanci University, Esra Erdem Sabanci University, Turkey
14:20
25m
Talk
Determining Action Reversibility in STRIPS Using Answer Set Programming with QuantifiersRemote
PADL
Wolfgang Faber University of Klagenfurt, Michael Morak University of Klagenfurt, Lukas Chrpa Czech Technical University in Prague