Event Calculus Research Articles

An event calculus production rule system for reasoning in dynamic and uncertain domains

AbstractAction languages have emerged as an important field of knowledge representation for reasoning about change and causality in dynamic domains. This paper presents Cerbere, a production system designed to perform online causal, temporal and epistemic reasoning based on the Event Calculus. The framework implements the declarative semantics of the underlying logic theories in a forward-chaining rule-based reasoning system, coupling the high expressiveness of its formalisms with the efficiency of rule-based systems. To illustrate its applicability, we present both the modeling of benchmark problems in the field, as well as its utilization in the challenging domain of smart spaces. A hybrid framework that combines logic-based with probabilistic reasoning has been developed, that aims to accommodate activity recognition and monitoring tasks in smart spaces.

Service Composition and Execution Plan Generation of Composite Semantic WEB Services Using Abductive Event Calculus

Web Service composition is indispensable, as a single web service cannot satisfy the complex functional requirement of a user. The two key challenges of semantic web service composition are the discovery of the most relevant atomic services from the composite semantic web services and by no means we can assure the execution of the composed atomic services in a proper order. In this work, these two challenges are addressed, and a novel architecture is proposed for atomic service discovery, composition and automatic plan generation for the proper execution of its candidate services. The proposed architecture takes the advantage of abductive event calculus that uses abductive theorem prover to generate a plan for the proper order of execution of the atomic services. The research has found that the plan generated by the proposed architecture is sound and complete.

On Mathematical Proving

Abstract This paper outlines a logical representation of certain aspects of the process of mathematical proving that are important from the point of view of Artificial Intelligence. Our starting-point is the concept of proof-event or proving, introduced by Goguen, instead of the traditional concept of mathematical proof. The reason behind this choice is that in contrast to the traditional static concept of mathematical proof, proof-events are understood as processes, which enables their use in Artificial Intelligence in such contexts, in which problem-solving procedures and strategies are studied. We represent proof-events as problem-centered spatio-temporal processes by means of the language of the calculus of events, which captures adequately certain temporal aspects of proof-events (i.e. that they have history and form sequences of proof-events evolving in time). Further, we suggest a “loose” semantics for the proof-events, by means of Kolmogorov’s calculus of problems. Finally, we expose the intented interpretations for our logical model from the fields of automated theorem-proving and Web-based collective proving.

Incremental learning of event definitions with Inductive Logic Programming

Event recognition systems rely on knowledge bases of event definitions to infer occurrences of events in time. Using a logical framework for representing and reasoning about events offers direct connections to machine learning, via Inductive Logic Programming (ILP), thus allowing to avoid the tedious and error-prone task of manual knowledge construction. However, learning temporal logical formalisms, which are typically utilized by logic-based event recognition systems is a challenging task, which most ILP systems cannot fully undertake. In addition, event-based data is usually massive and collected at different times and under various circumstances. Ideally, systems that learn from temporal data should be able to operate in an incremental mode, that is, revise prior constructed knowledge in the face of new evidence. In this work we present an incremental method for learning and revising event-based knowledge, in the form of Event Calculus programs. The proposed algorithm relies on abductive–inductive learning and comprises a scalable clause refinement methodology, based on a compressive summarization of clause coverage in a stream of examples. We present an empirical evaluation of our approach on real and synthetic data from activity recognition and city transport applications.

An Event Calculus for Event Recognition

Systems for symbolic event recognition accept as input a stream of time-stamped events from sensors and other computational devices, and seek to identify high-level composite events, collections of events that satisfy some pattern. RTEC is an Event Calculus dialect with novel implementation and ‘windowing’ techniques that allow for efficient event recognition, scalable to large data streams. RTEC supports the expression of rather complex events, such as ‘two people are fighting’, using simple primitives. It can operate in the absence of filtering modules, as it is only slightly affected by data that are irrelevant to the events we want to recognise. Furthermore, RTEC can deal with applications where event data arrive with a (variable) delay from, and are revised by, the underlying sources. RTEC can update already recognised events and recognise new events when data arrive with a delay or following data revision. We evaluate RTEC both theoretically, presenting a complexity analysis, and experimentally, using two real-world applications. The evaluation shows that RTEC can support real-time event recognition and is capable of meeting the performance requirements identified in a survey of event processing use cases.

Probabilistic Event Calculus for Event Recognition

Symbolic event recognition systems have been successfully applied to a variety of application domains, extracting useful information in the form of events, allowing experts or other systems to monitor and respond when significant events are recognised. In a typical event recognition application, however, these systems often have to deal with a significant amount of uncertainty. In this article, we address the issue of uncertainty in logic-based event recognition by extending the Event Calculus with probabilistic reasoning. Markov logic networks are a natural candidate for our logic-based formalism. However, the temporal semantics of the Event Calculus introduce a number of challenges for the proposed model. We show how and under what assumptions we can overcome these problems. Additionally, we study how probabilistic modelling changes the behaviour of the formalism, affecting its key property—the inertia of fluents. Furthermore, we demonstrate the advantages of the probabilistic Event Calculus through examples and experiments in the domain of activity recognition, using a publicly available dataset for video surveillance.

Recognising lifestyle activities of diabetic patients with a smartphone

Diabetes is both heavily affected by the patients’ lifestyle, and it affects their lifestyle. Most diabetic patients can manage the disease without technological assistance, so we should not burden them with technology unnecessarily, but lifestylemonitoring technology can still be beneficial both for patients and their physicians. Because of that we developed an approach to lifestyle monitoring that uses the smartphone, which most patients already have. The approach consists of three steps. First, a number of features are extracted from the data acquired by smartphone sensors, such as the user’s location from GPS coordinates and visible wi-fi access points, and the physical activity from accelerometer data. Second, several classifiers trained by machine learning are used to recognise the user’s activity, such as work, exercise or eating. And third, these activities are refined by symbolic reasoning encoded in Event Calculus. The approach was trained and tested on five people who recorded their activities for two weeks each. Its classification accuracy was 0.88.

Corruptible social agents

AbstractAgent‐based virtual simulations of social systems susceptible to corruption (e.g., police agencies) require agents capable of exhibiting corruptible behaviors to achieve realistic simulations and enable the analysis of corruption as a social problem. This paper proposes a formal belief‐desire‐intention framework supported by the functional event calculus and fuzzy logic for modeling corruption based on the integrity level of social agents and the influence of corrupters on them. Corruptible social agents are endowed with beliefs, desires, intentions, and corrupt‐prone plans to achieve their desires. This paper also proposes a fuzzy logic system to define the level of impact of corruption‐related events on the degree of belief in the truth of anti‐corruption factors (e.g., the integrity of the leader of an organization). Moreover, an agent‐based model of corruption supported by the proposed belief‐desire‐intention framework and the fuzzy logic system was devised and implemented. Results obtained from agent‐based simulations are consistent with actual macro‐level patterns of corruption reported in the literature. The simulation results show that (i) the bribery rate increases as more external entities attempt to bribe agents and (ii) the more anti‐corruption factors agents believe to be true, the less prone to perpetrate acts of corruption. Copyright © 2014 John Wiley & Sons, Ltd.

Heuristic strategies for assessing wireless sensor network resiliency: an event-based formal approach

Wireless sensor networks (WSNs) are increasingly being adopted in critical applications. In these networks undesired events may undermine the reliability level; thus their effects need to be properly assessed from the early stages of the development process onwards to minimize the chances of unexpected problems during use. In this paper we propose two heuristic strategies: what-if analysis and robustness checking. They allow to drive designers towards optimal WSN deployment solutions, from the point of view of the connection and data delivery resiliency, exploiting a formal approach based on the event calculus formal language. The heuristics are backed up by a support tool aimed to simplify their adoption by system designers. The tool allows to specify the target WSN in a user-friendly way and it is able to elaborate the two heuristic strategies by means of the event calculus specifications automatically generated. The WSN reliability is assessed computing a set of specific metrics. The effectiveness of the strategies is shown in the context of three case studies.

A probabilistic logic programming event calculus

AbstractWe present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of an LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.

Multi-agent Intelligent Distance Learning System

We consider an approach for constructing the security policy verification system intended for detection and resolution of conflicts in computer network security policy specifications. The architecture of the security policy verification system suggested is considered. The models of two verification modules are proposed. The first one is based on proof theory, namely Event Calculus, and uses abductive reasoning. The second module uses model checking technique. The current implementation of the security policy verification system is described.

Monitoring business constraints with the event calculus

Today, large business processes are composed of smaller, autonomous, interconnected subsystems, achieving modularity and robustness. Quite often, these large processes comprise software components as well as human actors, they face highly dynamic environments and their subsystems are updated and evolve independently of each other. Due to their dynamic nature and complexity, it might be difficult, if not impossible, to ensure at design-time that such systems will always exhibit the desired/expected behaviors. This, in turn, triggers the need for runtime verification and monitoring facilities. These are needed to check whether the actual behavior complies with expected business constraints, internal/external regulations and desired best practices. In this work, we present Mobucon EC, a novel monitoring framework that tracks streams of events and continuously determines the state of business constraints. In Mobucon EC, business constraints are defined using the declarative language Declare. For the purpose of this work, Declare has been suitably extended to support quantitative time constraints and non-atomic, durative activities. The logic-based language Event Calculus (EC) has been adopted to provide a formal specification and semantics to Declare constraints, while a light-weight, logic programming-based EC tool supports dynamically reasoning about partial, evolving execution traces. To demonstrate the applicability of our approach, we describe a case study about maritime safety and security and provide a synthetic benchmark to evaluate its scalability.

A Bounded Model Checking Approach for the Verification of Web Services Composition

In this paper, we propose a bounded model-checking based approach for the verification of declarative Web services composition processes using satisfiability solving (SAT). The need for the bounded model-checking approach stems from the nature of declarative processes as they are defined by only specifying the constraints that mark the boundary of the solution to the composition process. The proposed approach relies on using Event Calculus (EC) as the modeling formalism with a sound and complete EC to SAT encoding process. The use of EC as the modeling also formalism allows for a highly expressive approach for both the specification of composition model and for the specification of verification properties. Furthermore, as the conflict clauses returned by the SAT solver can be significantly large for complex processes and verification requirements, we propose a filtering criterion and defined patterns for identifying the clauses of interest for process verification.

A Probabilistic Propositional Logic System is an Event Semantics for Classical Formal System of Propositional Calculus

The well formed formulas (wffs) in classical formal system of propositional calculus (CPC) are only some formal symbols, whose meanings are given by an interpretation. A probabilistic logic system, based on a probabilistic space, is an event semantics for CPC, in which set operations are the semantic interpretations for connectives, event functions are the semantic interpretations for wffs, the event (set) inclusion is the semantic interpretation for tautological implication, and the event equality = is the semantic interpretation for tautological equivalence. CPC is applicable to probabilistic propositions completely. Event calculus instead of truth value (probability) calculus can be performed in CPC because there arent truth value functions (operators) to interpret all connectives correctly.

ER designer toolkit: a graphical event definition authoring tool

Modern assistive environments have the ability to collect data from various distributed sources and need to react swiftly to changes. As information flows, in the form of simple, source events, it becomes more and more difficult to quickly analyze the collected data in an automated way and transform them into operational knowledge. Event recognition (ER) addresses this problem. Several tools exist for defining ER rules, but only a few of them offer graphical design environments. Each such tool supports a single ER language, either query-based or rule-based. Also, many of these systems do not support the addition of user-defined operators, thus limiting the flexibility in rule design. This paper presents the Event Recognition Designer Toolkit (ERDT), a graphical authoring tool, with which a domain expert can design event recognition rules and produce standalone ER. The goal was to develop a user-friendly graphical tool with a basic set of operators, so that a user could easily produce recognizers for different domains and, when needed, easily extend the tool in order to satisfy domain-specific requirements. The ERDT uses an extendable pool of ER language libraries (at the moment SQL and Event Calculus are supported) and transforms the designed rules into Event Recognizers that use the preferred ER language. The same rule can be expressed in different languages without any changes to the design. Furthermore, the authoring tool is cross platform, free, and open source, so that it can be shared with the community, maximizing its potential impact and possible extension.

Axiomatization of Socio-Economic Principles for Self-Organizing Institutions

We address the problem of engineering self-organizing electronic institutions for resource allocation in open, embedded, and resource-constrained systems. In such systems, there is decentralized control, competition for resources and an expectation of both intentional and unintentional errors. The “optimal” distribution of resources is then less important than the endurance of the distribution mechanism. Under these circumstances, we propose to model resource allocation as a common-pool resource management problem, and develop a formal characterization of Elinor Ostrom’s socio-economic principles for self-governing institutions. This article applies a method for sociologically inspired computing to give a complete axiomatization of six of Ostrom’s eight principles in the Event Calculus. A testbed is implemented for experimenting with the axiomatization. The experimental results show that these principles support enduring institutions, in terms of longevity and membership, and also provide insight into calibrating the transaction and running costs associated with implementing the principles against the behavioral profile of the institutional membership. We conclude that it is possible to express Ostrom’s principles in logical form and that they are necessary and sufficient conditions for enduring self-organizing electronic institutions to manage sustainable common-pool resources.

Towards a satisfactory conversion of messages among agent-based information systems

Over the last years, there has been a change of perspective concerning the management of information systems, since they are no longer isolated and need to communicate with others. However, from a semantic point of view, real communication is difficult to achieve due to the heterogeneity of the systems. We present a proposal which, considering information systems are represented by software agents, provides a framework that favors a semantic communication among them, overcoming the heterogeneity of their agent communication languages. The main components of the framework are a suite of ontologies – conceptualizing communication acts – that will be used for generating the communication conversion, and an Event Calculus interpretation of the communications, which will be used for formalizing the notion of a satisfactory conversion. Moreover, we present a motivating example in order to complete the explanation of the whole picture.

Representing and monitoring social commitments using the event calculus

Multiagent social commitments provide a principled basis for agent interactions, and serve as a natural tool to resolve design ambiguities. Indeed, they have been the subject of considerable research for more than a decade. However, the take-up of the social commitments paradigm is yet to come. To explain this negative result, we pinpoint a number of shortcomings, which this article aims to address. We extend current commitment modelling languages, thus leveraging expressive possibilities that were precluded by previous formalizations. We propose a novel axiomatization of commitment operations in a first order Event Calculus framework, that accommodates reasoning with data and metric time. Finally, we illustrate how publicly available $${\mathcal{REC}}$$ implementations can be exploited for commitment monitoring purposes.

Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming

Circumscription and logic programs under the stable model semantics are two well-known nonmonotonic formalisms. The former has served as a basis of classical logic based action formalisms, such as the situation calculus, the event calculus and temporal action logics; the latter has served as a basis of a family of action languages, such as language A and several of its descendants. Based on the discovery that circumscription and the stable model semantics coincide on a class of canonical formulas, we reformulate the situation calculus and the event calculus in the general theory of stable models. We also present a translation that turns the reformulations further into answer set programs, so that efficient answer set solvers can be applied to compute the situation calculus and the event calculus.

An Efficient Formal Framework for Intrusion Detection Systems

Traffic anomalies and attacks are commonplace in today's networks, and identifying them rapidly and accurately is critical for large network operators. Intrusion detection systems are an important component of defensive measures protecting computer systems and networks from abuse.For an intrusion detection system, it is important to detect previously known attacks with high accuracy. However, detecting previously unseen attacks is equally important in order to minimize the losses as a result of a successful intrusion. It is also equally important to detect attacks at an early stage in order to minimize their impact. To address these challenges, this paper proposes to improve the efficiency of the network intrusion detection process by including an Event Calculus based specification to detect the registered and expected behaviour of the whole network.