Qualitative Spatial Reasoning Using Answer Set Programming

Description

Spatial (and temporal) information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as naval traffic monitoring, warehouse process optimisation and robot manipulation. Well over 40 qualitative calculi have been proposed so far, including Allen's interval algebra and the Region Connection Calculus. Reasoning with such calculi has been the focus of extensive research within the wider AI community, with a number of specialised reasoning tools developed. One barrier to the wide adoption of these tools is that only qualitative reasoning is supported natively, when real-world problems most often require a combination of qualitative and other forms of reasoning.

In this talk, I will discuss research to overcome this barrier, conducted at the University of Huddersfield, UK and the University of Calabria, Italy. Research focuses on using Answer Set Programming (ASP) as a unified formalism to tackle problems that require qualitative reasoning in addition to non-qualitative reasoning. ASP is a logic-based knowledge representation and reasoning approach that includes a rich but simple modeling language and is capable of handling search problems of high complexity. Research is motivated by two case studies: reasoning about the relations among large numbers of trajectories and determining optimal coverage of telecommunication antennas.

Period	21 Apr 2020
Held at	University of California, Santa Barbara, United States, California
Degree of Recognition	National