Event Calculus Research Articles

Extended Event Calculus using Neutrosophic Logic: Method, Implementation, Analysis, Recent Progress and Future Directions


 Brains do not reason as digital computers do. Computers reason in clear steps with statements that are either true or false, while humans reason with vague terms of common sense. Neutrosophy is a new branch of philosophy and machine intelligence that deals with neutralities, specifically the idea of indeterminacy that is evident and experienced in our everyday lives. Indeterminacy is interpreted as everything that falls between a concept, idea, statement, declaration, etc. and its opposite. The fundamental thesis of neutrosophy is to employ neutrosophic logic, an extension of fuzzy logic, to incorporate fuzzy truth into complex schemes of formal reasoning. Event calculus is a logical formalism used to describe and reason about events and their consequences over time. It is considered a valuable mathematical tool in the field of artificial intelligence (AI) for depicting dynamic systems where events occur and have temporal relationships with each other. However, previous studies in AI have neglected to adequately address the complexity of time. In this context, our work aims to introduce a neutrosophic event-based calculus as a logic formalism to handle situations where there is insufficient knowledge or ambiguity regarding the occurrence or consequences of certain events in a system. In particular, neutrosophic event calculus examines causality between ideas and the connection between tasks and actions in the presence of time. Due to the lack of related studies in the existing literature, we believe that our work will contribute to the field of knowledge representation by proposing an alternative to current forms of logic. We aim to demonstrate the capacity of neutrosophic event calculus in the context of knowledge representation and reasoning.

Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity

Symbolic event recognition systems detect event occurrences using first-order logic rules. Although existing online structure learning approaches ease the discovery of such rules in noisy data streams, they assume the existence of fully labelled training data. Splice is a recent online graph-based approach that estimates the labels of unlabelled data and makes it possible to learn such rules from semi-supervised training sequences of logical interpretations. However, Splice labelling depends significantly on the metric used to compute the distances of unlabelled examples to their labelled counterparts. Moreover, there is no guarantee about the quality of the labelling found in the local graphs that are built as the data stream in. In this paper, we propose a new online learning method, which includes an enhanced hybrid measure that combines an optimised structural distance, and a data-driven one. The former is guided by feature selection targeted to kNN classification, while the latter is a mass-based dissimilarity. Additionally, the enhanced Splice method, improves the graph construction process, by storing a synopsis of the past, in order to achieve more informed labelling on the local graphs. We evaluate our approach by learning Event Calculus theories for the tasks of human activity recognition, maritime monitoring, and fleet management. The evaluation suggests that our approach outperforms its predecessor, in terms of inferring the missing labels and improving the predictive accuracy of the underlying structure learning system.

An answer set programming-based implementation of epistemic probabilistic event calculus

We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent's own action executions and environmentally triggered events. Some of the agent's actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC's own declarative, Bayesian-inspired semantics.

Ontology-based hybrid commonsense reasoning framework for handling context abnormalities in uncertain and partially observable environments

Ambient intelligence (AmI) systems aim to provide users with context-aware assistance services intended to improve the quality of their lives in terms of autonomy, safety, and well-being. Taking the uncertainty and partial observability of these environments into account is of major importance for context recognition and, more specifically, to detect and solve context abnormalities such as those related to the user's behavior or those related to context attribute prediction. In this paper, an ontology-based framework integrating machine learning and probabilistic planning within commonsense reasoning is proposed to recognize the user's context and abnormalities associated with it. The reasoning is performed using event calculus in answer set programming (ECASP); ECASP allows for abductive and temporal reasoning, which results in an eXplainable AI (XAI) approach. A context ontology is proposed to axiomatize the reasoning and introduce the notion of probabilistic fluents into the EC formalism in order to perform probabilistic reasoning. The reasoning incorporates probabilistic planning based on a partially observable Markov decision process (POMDP) to solve knowledge incompleteness. To evaluate the proposed framework, real-life scenarios, based on the Orange4Home and SIMADL public datasets are implemented and discussed.

A PSL-based Approach to Human Activity Recognition in Smart Home Environments

Human activity recognition is widely used in smart cities, public safety and other fields, especially in smart home systems where it has a pivotal role. The study addresses the shortcomings of Markov logic networks for human activity recognition and proposes a human activity recognition method in smart home scenarios - an activity recognition framework based on Probabilistic Soft Logic (PSL). The framework is able to deal with logical uncertainty problems and provides expression and inference mechanisms for data uncertainty problems on this basis. The framework utilizes Deng entropy evidence theory to provide an evaluation method for sensor event uncertainty, and combines event calculus for activity modeling. Comparing the PSL method with three other common recognition methods, Ontology, Hidden Markov Model (HMM), and Markov logic network, on a public dataset, it was found that the PSL method has a much better ability to handle data uncertainty than the other three algorithms. The average recognition rates on the ADL and ADL-E sub datasets were 82.87% and 80.33%, respectively. In experiments to verify the ability of PSL to handle temporal complexity, PSL showed the least significant decrease in the average recognition rate and maintained an average recognition rate of 81.02% in the presence of concurrent and alternating activities. The human activity recognition method based on PSL has a better performance in handling both data uncertainty and temporal complexity.

An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems

AbstractWe present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the rehabilitation of children with neuromotor disorders. In this application, children perform a rehabilitation task in the form of games. The main aim of the system is to derive a set of parameters the child’s current level of cognitive and behavioral performance (e.g., engagement, attention, task accuracy) from the available sensors and classifiers (e.g., eye trackers, motion sensors, emotion recognition techniques) and take decisions accordingly. These decisions are typically aimed at improving the child’s performance by triggering appropriate re-engagement stimuli when their attention is low, by changing the game or making it more difficult when the child is losing interest in the task as it is too easy. Alongside state-of-the-art techniques for emotion recognition and head pose estimation, we use a runtime variant of a probabilistic and epistemic logic programming dialect of the Event Calculus, known as the Epistemic Probabilistic Event Calculus. In particular, the probabilistic component of this symbolic framework allows for a natural interface with the machine learning techniques. We overview the architecture and its components, and show some of its characteristics through a discussion of a running example and experiments.

On Modelling Process Aspects With Deontic Event-Calculus

Intuitive and faithful modelling the compliance requirements about the process aspects is a prerequisite for their automated compliance checking. Several formalisms with varying degrees of expressiveness for modelling compliance requirements have been reported in the literature. Deontic Event-Calculus (DEC) is a normative variant of Event-Calculus (EC) formalism with predicates to modelled normative requirements. However, currently, DEC does not support capturing normative requirements about the process aspects. In this paper, we extend DEC with new deontic predicates to model process aspects of data, time, control flow, and resources. The extended deontic predicates enable DEC to intuitively represent the compliance requirements relevant to aspects of a business process. Besides, we report the complexity evaluation of the extended deontic predicates using well-known Halstead’s complexity metrics. Evaluation result demonstrates that the complexity of modelling the compliance rules with DEC predicates is significantly lower even when the complexity of the standard EC is exponential.

An Intuitionistic Calculus to Complex Abnormal Event Recognition on Data Streams

Data mining in real-time data streams is associated with multiple types of uncertainty, which often leads the respective categorizers to make erroneous predictions related to the presence or absence of complex events. But recognizing complex abnormal events, even those that occur in extremely rare cases, offers significant support to decision-making systems. Therefore, there is a need for robust recognition mechanisms that will be able to predict or recognize when an abnormal event occurs or will occur on a data stream. Considering this need, this paper presents an Intuitionistic Tumbling Windows event calculus (ITWec) methodology. It is an innovative data analysis system that combines for the first time in the literature a set of multiple systems for Complex Abnormal Event Recognition (CAER). In the proposed system, the probabilities of the existence of a high-level complex abnormal event for each period are initially calculated nonparametrically, based on the probabilities of the low-level events associated with it. Because cumulative results are sought in consecutive, nonoverlapping sections of the data stream, the method uses the clearly defined rules of initialization and termination of the tumbling windows method, where there is an explicit determination of the time interval within which several blocks of a particular stream are investigated window. Finally, the number of maximum probable intervals in which an event is likely to occur based on a certain probability threshold is calculated, based on a parametric representation of intuitively fuzzy sets.

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.

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

AbstractAutomated 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.

Recombinable Game Mechanics for Automated Design Support

Systems that provide automated game-design support — whether fully automated game generators, or tools to assist human designers — must be able to maintain a representation of a game design and add or remove game mechanics to support incrementally modifying the game. The system should then be able to reason about the design to support the designer. For example, it might point out that the set of mechanics makes the game unwinnable; or that there's only one complex possible way to beat the game; or that some room is impossible to get to. In addition, the same representation should be actually playable as a game. Existing game representations encode a fairly narrow range of games, most commonly symmetric board games; these representations are also difficult to extend or revise. We propose an architecture based on the event calculus, a logical representation designed for reasoning about time in an elaboration-tolerant way, meaning that designs can be changed by adding or removing sets of axioms rather than modifying brittle hard-coded representations. The resulting game design is a declarative specification in formal logic, so can be critiqued by making queries that are answered through logical inference. Since it specifies the game's simulation rules, it may be executed by logical inference as well; if symbols specifying input and graphical representation are appropriately mapped to input devices and a graphical display, the same declarative representation can be executed as a fully interactive, graphical game. We describe how to organize a library of game-design mechanics using this event-calculus framework, and describe a simple tile-based game, showing how it can be easily modified, critiqued and debugged, and played.

Knowledge-Assisted Reasoning of Model-Augmented System Requirements with Event Calculus and Goal-Directed Answer Set Programming

We consider requirements for cyber-physical systems represented in constrained natural language. We present novel automated techniques for aiding in the development of these requirements so that they are consistent and can withstand perceived failures. We show how cyber-physical systems' requirements can be modeled using the event calculus (EC), a formalism used in AI for representing actions and change. We also show how answer set programming (ASP) and its query-driven implementation s(CASP) can be used to directly realize the event calculus model of the requirements. This event calculus model can be used to automatically validate the requirements. Since ASP is an expressive knowledge representation language, it can also be used to represent contextual knowledge about cyber-physical systems, which, in turn, can be used to find gaps in their requirements specifications. We illustrate our approach through an altitude alerting system from the avionics domain.

Learning explanations for biological feedback with delays using an event calculus

Learning explanations for biological feedback with delays using an event calculus

Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

AbstractComplex 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).

Inductive learning of answer set programs for autonomous surgical task planning

The quality of robot-assisted surgery can be improved and the use of hospital resources can be optimized by enhancing autonomy and reliability in the robot’s operation. Logic programming is a good choice for task planning in robot-assisted surgery because it supports reliable reasoning with domain knowledge and increases transparency in the decision making. However, prior knowledge of the task and the domain is typically incomplete, and it often needs to be refined from executions of the surgical task(s) under consideration to avoid sub-optimal performance. In this paper, we investigate the applicability of inductive logic programming for learning previously unknown axioms governing domain dynamics. We do so under answer set semantics for a benchmark surgical training task, the ring transfer. We extend our previous work on learning the immediate preconditions of actions and constraints, to also learn axioms encoding arbitrary temporal delays between atoms that are effects of actions under the event calculus formalism. We propose a systematic approach for learning the specifications of a generic robotic task under the answer set semantics, allowing easy knowledge refinement with iterative learning. In the context of 1000 simulated scenarios, we demonstrate the significant improvement in performance obtained with the learned axioms compared with the hand-written ones; specifically, the learned axioms address some critical issues related to the plan computation time, which is promising for reliable real-time performance during surgery.

Enabling situational awareness of business processes

PurposeThe purpose of this research is to explore the ways of integrating situational awareness into business process management for the purpose of realising hyper automated business processes. Such business processes will help improve their customer experiences, enhance the reliability of service delivery and lower the operational cost for a more competitive and sustainable business.Design/methodology/approachOntology has been deployed to establish the context modelling method, and the event handling mechanisms are developed on the basis of event calculus. An approach on performance of the proposed approach has been evaluation by checking the cost savings from the simulation of a large number of business processes.FindingsIn this research, the authors have formalised the context presentation for a business process with a focus on rules and entities to support context perception; proposed a system architecture to illustrate the structure and constitution of a supporting system for intelligent and situation aware business process management; developed real-time event elicitation and interpretation mechanisms to operationalise the perception of contextual dynamics and real-time responses; and evaluated the applicability of the proposed approaches and the performance improvement to business processes.Originality/valueThis paper presents a framework covering process context modelling, system architecture and real-time event handling mechanisms to support situational awareness of business processes. The reported research is based on our previous work on radio frequency identification-enabled applications and context-aware business process management with substantial extension to process context modelling and process simulation.

Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem

Key challenges in Internet-of-Things (IoT) system design and management include the secure system composition and the calculation of the security and dependability level of the final system. This paper presents an event-based model-checking framework for IoT systems’ design and management, called CompoSecReasoner. It invokes two main functionalities: (i) system composition verification, and (ii) derivation and validation of security, privacy, and dependability (SPD) metrics. To measure the SPD values of a system, we disassemble two well-known types of security metrics—the attack surface methodologies and the medieval castle approach. The first method determines the attackable points of the system, while the second one defines the protection level that is provided by the currently composed system-of-systems. We extend these techniques and apply the Event Calculus method for modelling the dynamic behavior of a system with progress in time. At first, the protection level of the currently composed system is calculated. When composition events occur, the current system status is derived. Thereafter, we can deploy reactive strategies and administrate the system automatically at runtime, implementing a novel setting for Moving Target Defenses. We demonstrate the overall solution on a real ambient intelligence application for managing the embedded devices of two emulated smart buildings.

Probabilistic reasoning about epistemic action narratives

We propose the action language EPEC – Epistemic Probabilistic Event Calculus – that supports probabilistic, epistemic reasoning about narratives of action occurrences and environmentally triggered events, and in particular facilitates reasoning about future belief-conditioned actions and their consequences in domains that include both perfect and imperfect sensing actions. To provide a declarative semantics for sensing and belief conditioned actions in a probabilistic, narrative setting we introduce the novel concept of an epistemic reduct. We then formally compare our language with two established frameworks for probabilistic reasoning about action – the action language PAL by Baral et al., and the extension of the situation calculus to reason about noisy sensors and effectors by Bacchus et al. In both cases we prove a correspondence with EPEC for a class of domains representable in both frameworks.

Modeling artificial agents’ actions in context – a deontic cognitive event ontology

Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts about knowledge, beliefs, perceptions and communication; 3) an ontology concerning future intentions, desires, and aversions; and, finally, 4) a deontic ontology for modeling obligations and prohibitions which limit agents’ actions. The architecture of the ontology framework is inspired by deontic cognitive event calculus as well as epistemic and deontic logic. We also describe a case study in which the proposed DCEO ontology supports autonomous vehicle navigation.

On Quantified Modal Theorem Proving for Modeling Ethics

In the last decade, formal logics have been used to model a wide range of ethical theories and principles with the goal of using these models within autonomous systems. Logics for modeling ethical theories, and their automated reasoners, have requirements that are different from modal logics used for other purposes, e.g. for temporal reasoning. Meeting these requirements necessitates investigation of new approaches for proof automation. Particularly, a quantified modal logic, the deontic cognitive event calculus (DCEC), has been used to model various versions of the doctrine of double effect, akrasia, and virtue ethics. Using a fragment of DCEC, we outline these distinct characteristics and present a sketches of an algorithm that can help with some aspects proof automation for DCEC.