Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming

Automated commonsense reasoning (CR) is essential for building human-like AI systems featuring, for example, explainable AI. Event calculus (EC) is a family of formalisms that model CR with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g. time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.

Formalizing commitments using the event calculus and RuleML

Smart Contracts enable the automated execution of exchanges on the blockchain. From an ontological perspective, smart contracts create and automate the fulfillment of social commitments between actors. Whereas traditional deontic logic is used to make a legal determination in contractual multi-actor interactions, this paper focuses on the consequences of these actions resulting from that determination, thereby shifting the focus from monitoring to execution. The interactions between actors and the consequences in terms of commitments have not yet been formalized for smart contracts. The perspective of smart contracts is interesting, since they are considered to be autonomous agents, able to generate automated actions. We use the Event Calculus as a formal logic to represent and reason about the effects of these automated actions and the resulting commitments. Since the Event Calculus deals with local events and the consideration of time, this approach enables the uniform representation of commitments, including their operations and reasoning rules.

Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER data sets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).