A Generalised Approach for Encoding and Reasoning with Qualitative Theories in Answer Set Programming

Abstract

Qualitative reasoning involves expressing and deriving knowledge based on qualitative terms such as natural language expressions, rather than strict mathematical quantities. Well over 40 qualitative calculi have been proposed so far, mostly in the spatial and temporal domains, with several practical applications such as naval traffic monitoring, warehouse process optimisation and robot manipulation. Even if a number of specialised qualitative reasoning tools have been developed so far, an important barrier to the wider 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 work, we propose to overcome this barrier by using ASP as a unifying formalism to tackle problems that require qualitative reasoning in addition to non-qualitative reasoning. A family of ASP encodings is proposed which can handle any qualitative calculus with binary relations. These encodings are experimentally evaluated using a real-world dataset based on a case study of determining optimal coverage of telecommunication antennas, and compared with the performance of two well-known dedicated reasoners. Experimental results show that the proposed encodings outperform one of the two reasoners, but fall behind the other, an acceptable trade-off given the added benefits of handling any type of reasoning as well as the interpretability of logic programs.

Understanding the influential and mediating role of cultural enablers of AI integration to supply chain

“…For example, machine learning can support data-driven decisionmaking in pre-production, production, processing, and distribution stages of agricultural SCs (Sharma et al 2020). Prior research has examined the role of AI for effective and efficient SCM, including forecasting (Chien, Lin, and Lin 2020), configuration and optimisation (Abbasi et al 2020;Fragapane et al 2020), forecasting (Chien, Lin, and Lin 2020), risk management (Baryannis et al 2020;Soleymani and Nejad 2018), col-laboration between human operators and AI-based systems (Klumpp 2018), increased operational efficiency in replenishment policies (Priore et al 2019), and supplier selection (Zhao and Yu 2011;Choy et al 2004). While such studies have made valuable contributions, knowledge about the influential and mediating role of culture is limited.…”

Modal Logic S5 Satisfiability in Answer Set Programming

“…The motivation behind this proposal lies in the close relationship between ASP and SAT and the readability and configurability afforded by ASP encodings due to their logic programming nature (Baryannis et al . 2018;2020). We first provide a complete ASP encoding of the S5 normal form introduced by Huang et al .…”