Event Calculus Research Articles

지각 정보를 활용한 이벤트 연산 기반 고령자 생활패턴 인지 연구

지각 정보를 활용한 이벤트 연산 기반 고령자 생활패턴 인지 연구

A History Based Logic for Dynamic Preference Updates

History based models suggest a process-based approach to epistemic and temporal reasoning. In this work, we introduce preferences to history based models. Motivated by game theoretical observations, we discuss how preferences can dynamically be updated in history based models. Following, we consider arrow update logic and event calculus, and give history based models for these logics. This allows us to relate dynamic logics of history based models to a broader framework.

A cognitive architecture for inner speech

A cognitive architecture for inner speech is presented. It is based on the Standard Model of Mind, integrated with modules for self-talking. Briefly, the working memory of the proposed architecture includes the phonological loop as a component which manages the exchanging information between the phonological store and the articulatory control system. The inner dialogue is modeled as a loop where the phonological store hears the inner voice produced by the hidden articulator process. A central executive module drives the whole system, and contributes to the generation of conscious thoughts by retrieving information from long-term memory. The surface form of thoughts thus emerges by the phonological loop. Once a conscious thought is elicited by inner speech, the perception of new context takes place and then repeating the cognitive loop. A preliminary formalization of some of the described processes by event calculus, and early results of their implementation on the humanoid robot Pepper by SoftBank Robotics are discussed.

Online Event Recognition from Moving Vehicles: Application Paper

AbstractWe present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency.

Revising event calculus theories to recover from unexpected observations

Recent extensions of the Event Calculus resulted in powerful formalisms, able to reason about a multitude of commonsense phenomena in causal domains, involving epistemic notions, functional fluents and probabilistic aspects, among others. Less attention has been paid to the problem of automatically revising (correcting) a Knowledge Base when an observation contradicts inferences made regarding the world state. Despite mature work on the related belief revision field, adapting such results for the case of action theories is non-trivial. This paper describes how to address this problem for deterministic, yet partially observable, domains, by proposing a generic framework in the context of the Event Calculus, along with ASP encodings of the revision algorithm and a web-based tester of the formalism implementation.

A probabilistic interval-based event calculus for activity recognition

Activity recognition refers to the detection of temporal combinations of ‘low-level’ or ‘short-term’ activities on sensor data. Various types of uncertainty exist in activity recognition systems and this often leads to erroneous detection. Typically, the frameworks aiming to handle uncertainty compute the probability of the occurrence of activities at each time-point. We extend this approach by defining the probability of a maximal interval and the credibility rate for such intervals. We then propose a linear-time algorithm for computing all probabilistic temporal intervals of a given dataset. We evaluate the proposed approach using a benchmark activity recognition dataset, and outline the conditions in which our approach outperforms time-point-based recognition.

An approach for recognition of human's daily living patterns using intention ontology and event calculus

This paper proposes a method of recognizing the intention of human activity based on percept sequences that represent the activities of daily living (ADL) in a residential space. Based on the activity intention ontology representing actions, poses, and nearby objects related to human activity intentions, the proposed method identifies a human activity intention by using the event calculus when a percept sequence is entered. It is very difficult to recognize ADL occurring in various places in a residence by using regular percept sequences without error. Furthermore, a human activity can have complex intentions. To solve these problems, this paper proposes an activity intention recognition process consisting of three steps. First, the activity intention inference step recognizes the intention of a given percept sequence based on the activity intention ontology. Second, the complex intention-identifying step determines whether to terminate the previously maintained activity intention or continue maintaining it in a complex manner based on the newly inferred activity intention. Third, the uncertainty handling step corrects an inaccurate activity intention caused by an error in the percept sequence. To evaluate the proposed method presented in this paper, activity intention recognition experiments were conducted based on the ADL data that were collected for six households of elderly persons older than 70 years. The results showed that the proposed method has a precision of 99.62% and recall of 92.83%.

Complex reactive event processing for assisted living: The Habitat project case study

While the increasing average age of population is posing new challenges to societies and healthcare systems, the emergence of the Internet of Things research area is generating the hope for automated assisted environments, which could combine the advances in sensors networks with that of runtime monitoring systems, in order to create smart houses able to take care of their older inhabitants and delay the recourse to hospitals and nursing homes. However, although various assisted living systems have been proposed in the last decade, the goal of realizing an effective domestic support system for elderly is still far from reached.In this work, we present a project aiming to re-engineer a set of everyday life objects, equipping them with environmental and wearable sensors, thus to monitor the condition of older people in their domestic residences and provide security while preserving the autonomy and independence of the subjects. The main focus of the paper at hand is on the requirements and solutions implemented to realize the backbone infrastructure of such system as regards both the adopted semantic message routing mechanism and the newly conceived approach to event analysis, which combines Complex Event Processing and a reactive implementation of Event Calculus.

Research on Self-Adaptive Method of Collaborative Mode Between Operational Organizations

Reliability of the collaboration among military organizations is the key to success or failure of joint operations. However, the battlefield environment is changing rapidly, and the collaborative mode between organizations is easily affected by the changes. Therefore, a flexible collaborative mechanism for adjustment must be proposed, and it requires higher flexibility and autonomy of collaborative organizations. In order to pursue a better collaboration and then accomplish the common mission, this paper firstly establishes a context-aware model based on ontology and event calculus, which is used for the perception and reasoning analysis of changes; secondly, designs two evaluation index systems respectively for operational organizations' overall effectiveness and capability of operational activities; lastly, combines the former models and proposes an adaptive collaborative method. The method evaluates the operational capability of an organization, compares it with a given threshold, and then adopts the solution of activity replacement or organization replacement to update the collaborative mode.

Parallel online event calculus learning for complex event recognition

Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing, among others, direct connections to machine learning, via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system that learns event definitions in the form of Event Calculus theories, in a single pass over a data stream. We present two strategies for parallel online learning with OLED. We evaluate our proposed approaches on three datasets from the domains of activity recognition and maritime surveillance and show that they can significantly reduce training times, while they are capable of achieving super-linear speed-ups under certain circumstances.

Indexing the Event Calculus: Towards practical human-readable Personal Health Systems.

Personal Health Systems (PHS) are mobile solutions tailored to monitoring patients affected by chronic non communicable diseases. In general, a patient affected by a chronic disease can generate large amounts of events: for example, in Type 1 Diabetic patients generate several glucose events per day, ranging from at least 6 events per day (under normal monitoring) to 288 per day when wearing a continuous glucose monitor (CGM) that samples the blood every 5 minutes for several days. Just by itself, without considering other physiological parameters, it would be impossible for medical doctors to individually and accurately follow every patient, highlighting the need of simple approaches towards querying physiological time series. Achieving this with current technology is not an easy task, as on one hand it cannot be expected that medical doctors have the technical knowledge to query databases and on the other hand these time series include thousands of events, which requires to re-think the way data is indexed. Anyhow, handling data streams efficiently is not enough. Domain experts' knowledge must be explicitly included into PHSs in a way that it can be easily readed and modified by medical staffs. Logic programming represents the perfect programming paradygm to accomplish this task. In this work, an Event Calculus-based reasoning framework to standardize and express domain-knowledge in the form of monitoring rules is suggested, and applied to three different use cases. However, if online monitoring has to be achieved, the reasoning performance must improve dramatically. For this reason, three promising mechanisms to index the Event Calculus Knowledge Base are proposed. All of them are based on different types of tree indexing structures: k-d trees, interval trees and red-black trees. The paper then compares and analyzes the performance of the three indexing techniques, by computing the time needed to check different type of rules (and eventually generating alerts), when the number of recorded events (e.g. values of physiological parameters) increases. The results show that customized jREC performs much better when the event average inter-arrival time is little compared to the checked rule time-window. Instead, where the events are more sparse, the use of k-d trees with standard EC is advisable. Finally, the Multi-Agent paradigm helps to wrap the various components of the system: the reasoning engines represent the agent minds, and the sensors are its body. The said agents have been developed in MAGPIE, a mobile event based Java agent platform.

Comodo: Collaborative monitoring of commitment delegations

Understanding accountability in contract violations, e.g., whom is accountable for what, is a tedious, time-consuming, and costly task for human decision-making, especially when contractual responsibilities are delegated among parties. Intelligent software agents equipped with expert capabilities such as monitoring and diagnosis help save time and improve accuracy of diagnosis by formal reasoning upon electronic contracts. Such contracts are represented as commitment norms, a well studied artifact in multi-agent systems, which provide semantics for agent interactions. Due to the open and heterogeneous nature of multi-agent systems, commitments are often violated. When a commitment is violated, e.g., an exception occurs, agents need to collaborate to understand what went wrong and which agent is responsible. We propose Comodo: a framework for monitoring commitment delegations and detecting violations. We define a complete set of possible rational delegation schemes for commitments, identifying for each combination of delegations what critical situations may lead to an improper delegation and potentially to a commitment violation. Comodo provides a sound and complete distributed reasoning procedure that is able to find all improper delegations of a given commitment. We provide the complete implementation of Comodo using the Reactive Event Calculus, and present an e-commerce case study to demonstrate its workings. Due to its generic nature, we discuss the application of our approach to other distributed diagnosis problems in emergency healthcare, Internet of Things and smart environments, and security, privacy, and accountability in the context of socio-technical systems.

A Novel Runtime Verification Solution for IoT Systems

Internet of Things (IoT) systems promise a seamless connected world with machines integrating their services without human intervention. It’s highly probable that the entities participating in such autonomous machine to machine interactions are to be provided by different manufactures. Thus, integrating such heterogeneous devices from many providers complicates design and verification of IoT systems at an unprecedented scale. In this paper, we propose a novel runtime verification approach for IoT systems. The contributions of our proposed solution include: exploiting the interactions in message sequence charts (MSC) to specify message exchanges of constrained application protocol-based IoT systems in terms of events, a novel event calculus for formally describing IoT system constraints specified by means of MSCs, and an event processing algebra that uses complex-event processing techniques for detecting failures in the system by monitoring the runtime event occurrences with respect to the system constraints defined by event calculus. We further demonstrate the viability of proposed solution with case studies.

Agent‐oriented activity recognition in the event calculus: An application for diabetic patients

AbstractWe present a knowledge representation framework on the basis of the Event Calculus that allows an agent to recognize complex activities from low‐level observations received by multiple sensors, reason about the life cycle of such activities, and take action to support their successful completion. Activities are multivalue fluents that change according to events that occur in the environment. The parameters of an activity consist of a unique label, a set of participants involved in the performing of the activity, and a unique goal associated with the activity revealing the activity's desired outcome. Our contribution is the identification of an activity life cycle describing how activities can be started, interrupted, suspended, resumed, or completed over time, as well as how these can be represented. The framework also specifies activity goals, their associated life cycle, and their relation with the activity life cycle. We provide the complete implementation of the framework, which includes an activity generator that automatically creates synthetic sensor data in the form of event streams that represent the everyday lifestyle of a type 1 diabetic patient. Moreover, we test the framework by generating very large activity streams that we use to evaluate the performance of the recognition capability and study its relative merits.

Cooperative decoupled processes

Event-driven programming has become a major paradigm in developing concurrent, distributed systems. Its benefits are often informally captured by the key tenet of “decoupling,” a notion which roughly captures the ability of processes to join and leave (or fail) applications dynamically, and to be developed by independent parties. Programming models for event-driven programming either make it hard to globally reason about control flow, thus hampering sound execution, or sacrifice decoupling to aid in reasoning about control flow. This work fills the gap by introducing a programming model—dubbed cooperative decoupled processes—that achieves both decoupling and global reasoning about control flow. We introduce this programming model through an event calculus, loosely inspired by the Join calculus, that enables reasoning about cooperative decoupled processes through the concepts of pre- and postconditions. A linear type system controls aliasing of events to avoid a break of control flow and thus safe exchange of shared events. Fundamental properties of the type system such as subject reduction, migration safety, and progress are established.

ARKHAM: An Advanced Refinement toolkit for Handling Service Level Agreements in Software-Defined Networking

Software-Defined Networking (SDN) provides a more sophisticated and flexible architecture for managing and monitoring network traffic. SDN moves part of the decision-making logic (i.e., flow processing and packet routing) from network devices into a logically centralized controller. However, the expected behavior and configuration of network devices are often defined directly in the controller as static rules for specific situations. This approach becomes an issue when associated with an increasing number of network elements, links, and services, resulting in a large amount of rules and a high overhead related to network configuration. As an alternative, techniques such as Policy-Based Network Management (PBNM) and more specifically policy refinement can be used by operators to write Service Level Agreements (SLAs) in a user-friendly interface without the need to manually reconfigure each network device. To address these issues, we specifically introduce ARKHAM: an Advanced Refinement Toolkit for Handling SLAs in SDN. In this article, we present (i) a policy authoring framework that uses logical reasoning for the specification of business-level goals and to automate their refinement; (ii) an OpenFlow controller which performs information gathering and configuration deployment; (iii) a policy repository that stores information about the behavior of the infrastructure, which is obtained by the OpenFlow Controller, and policy authoring operations; and (iv) a formal representation using event calculus that describes our solution. The main contributions of this work are (i) the capacity to deploy refined policies with minimal human intervention; (ii) analysis of the infrastructure's ability to fulfill the requirements of high-level policies; (iii) decreased amount of network rules coded into the controller; and (iv) management and deployment of new rules with minimal disruption to the network. The experimental results demonstrate that the refinement toolkit achieves the expected results within acceptable performance bounds, even with the increasing complexity and size of SLAs, network topologies, and repositories.

A Knowledge Oriented Approach for Composing Ambient Intelligence Services

Abstract: Ambient environments involve a variety of smart devices. Communities of these devices form a ubiquitous information processing system to which the physical environment should provide an intuitive and flexible user interface. Indeed, due to the proliferation of the number of services in ubiquitous environments, the problem of dynamic service composition becomes a complex task. Our work tries to solve the problem of service composition based on AI planning. The approach used in our framework is based on a temporal reasoning system called Event Calculus. This formalism is used to generate an action plan to a symbolic level from a composition plan described in XML modeling the behavior of the composition of services. Event Calculus has a double interest to propose a solution to the problem of temporal persistence and to provide a usable technique for planning. The framework for service composition using Event Calculus includes mechanism for the service selection, based on an estimation of the reputation for abstract services and the Quality of Service (QoS).

Creating a Model of an Operator of a Simulator Complex using Commonsense Reasoning

The task of creating a simulator complex is an urgent task in specific professional fields. To improve training efficiency it seems reasonable to create an intellectual agent able to make the best decisions in every situation during the training and to explain why a certain decision was made. The explanation should be obvious and clear for a trainee. The paper proposes a way for creating an operator model. It is based on event calculus. The event calculus includes methods to solve the frame problem, the ramification problem and the qualification problem. It allows solving deduction, abduction and postdiction problems, so the agent based on event calculus is able to make its own solution and judge a trainee's solution. As an example of simulator complex, we used the simulator complex for marshalling yard operators.

COMPOSE: Using temporal patterns for interpreting wearable sensor data with computer interpretable guidelines

This paper describes a novel temporal logic-based framework for reasoning with continuous data collected from wearable sensors. The work is motivated by the Metabolic Syndrome, a cluster of conditions which are linked to obesity and unhealthy lifestyle. We assume that, by interpreting the physiological parameters of continuous monitoring, we can identify which patients have a higher risk of Metabolic Syndrome. We define temporal patterns for reasoning with continuous data and specify the coordination mechanisms for combining different sets of clinical guidelines that relate to this condition. The proposed solution is tested with data provided by twenty subjects, which used sensors for four days of continuous monitoring. The results are compared to the gold standard. The novelty of the framework stands in extending a temporal logic formalism, namely the Event Calculus, with temporal patterns. These patterns are helpful to specify the rules for reasoning with continuous data and in combining new knowledge into one consistent outcome that is tailored to the patient's profile. The overall approach opens new possibilities for delivering patient-tailored interventions and educational material before the patients present the symptoms of the disease.

Online learning of event definitions

AbstractSystems for symbolic event recognition infer occurrences of events in time using a set of event definitions in the form of first-order rules. The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP). We present an ILP system for online learning of Event Calculus theories. To allow for a single-pass learning strategy, we use the Hoeffding bound for evaluating clauses on a subset of the input stream. We employ a decoupling scheme of the Event Calculus axioms during the learning process, that allows to learn each clause in isolation. Moreover, we use abductive-inductive logic programming techniques to handle unobserved target predicates. We evaluate our approach on an activity recognition application and compare it to a number of batch learning techniques. We obtain results of comparable predicative accuracy with significant speed-ups in training time. We also outperform hand-crafted rules and match the performance of a sound incremental learner that can only operate on noise-free datasets.