Context-aware Multi-agent System Research Articles

Utility-based context-aware multi-agent recommendation system for energy efficiency in residential buildings

A significant part of CO2 emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households’ energy efficiency. To nudge changes in energy consumption behavior, simple but powerful architectures are vital. This paper presents a novel algorithm of a recommendation system generating device usage recommendations and suggests a framework for evaluating its performance by analyzing potential energy cost savings. As a utility-based recommender system, it models user preferences depending on habitual device usage patterns, user availability, and device usage costs. As a context-aware system, it requires an external hourly electricity price signal and appliance-level energy consumption data. Due to a multi-agent architecture, it allows for easy integration of new agents, enabling seamless functionality expansion, or the disabling of existing agents to tailor the system to specific needs. Empirical results show that the system can provide energy cost savings of 18% and more for most studied households.

Modelling and Verification of Context-Aware Intelligent Assistive Formalism

Recent years have witnessed the expeditious evolution of intelligent smart devices and autonomous software technologies with the expanded domains of computing from workplaces to smart computing in everyday routine life activities. This trend has been rapidly advancing towards the new generation of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquire contextual information from the surrounding environment autonomously, perform reasoning on it, and then adapt their behaviors accordingly. With the proliferation of context-aware systems and smart sensors, real-time monitoring of environmental situations (context) has become quite trivial. However, it is often challenging because the imperfect nature of context can cause the inconsistent behavior of the system. In this paper, we propose a context-aware intelligent decision support formalism to assist cognitively impaired people in managing their routine life activities. For this, we present a semantic knowledge-based framework to contextualize the information from the environment using the protégé ontology editor and Semantic Web Rule Language (SWRL) rules. The set of contextualized information and the set of rules acquired from the ontology can be used to model Context-aware Multi-Agent Systems (CMAS) in order to autonomously plan all activities of the users and notify users to act accordingly. To illustrate the use of the proposed formalism, we model a case study of Mild Cognitive Impaired (MCI) patients using Colored Petri Nets (CPN) to show the reasoning process on how the context-aware agents collaboratively plan activities on the user's behalf and validate the correctness properties of the system.

Recommendation of Workplaces in a Coworking Building: A Cyber-Physical Approach Supported by a Context-Aware Multi-Agent System.

Recommender systems are able to suggest the most suitable items to a given user, taking into account the user’s and item`s data. Currently, these systems are offered almost everywhere in the online world, such as in e-commerce websites, newsletters, or video platforms. To improve recommendations, the user’s context should be considered to provide more accurate algorithms able to achieve higher payoffs. In this paper, we propose a pre-filtering recommendation system that considers the context of a coworking building and suggests the best workplaces to a user. A cyber-physical context-aware multi-agent system is used to monitor the building and feed the pre-filtering process using fuzzy logic. Recommendations are made by a multi-armed bandit algorithm, using -greedy and upper confidence bound methods. The paper presents the main results of simulations for one, two, three, and five years to illustrate the use of the proposed system.

Semantic Knowledge Transformation for Context-aware Heterogeneous Formalisms

In recent years, an increasing social dependency has been observed over the cell phones and now evolved into smart devices. Due to the rapid escalation of these smart devices, users are becoming habitual in utilizing these services using smart-phones and /or wearable devices in which different applications are running to assist and facilitate users in daily life routine activities. Mobility and context-awareness are the core features of pervasive computing. Context-awareness has the capability to identify the current situation and respond accordingly in the environment whenever and wherever needed. However, it is quite challenging to detect and sense the more appropriate contextual information when various interactive devices communicate among themselves. This paper presents the semantic knowledge transfor-mation techniques for ontology-driven context-aware formalisms to model heterogeneous systems. We propose theoretical as well as practical approaches to transform semantic knowledge into first-order Horn-clause rules format which can be used by context-aware multi-agent systems to achieve their desired goals.

A Logical Framework for the Representation and Verification of Context-aware Agents

We propose a logical framework for modelling and verifying context-aware multi-agent systems. We extend CTL? with belief and communication modalities, and the resulting logic ??OCRS allows us to describe a set of rule-based reasoning agents with bound on time, memory and communication. The set of rules which are used to model a desired systems is derived from OWL 2 RL ontologies. We provide an axiomatization of the logic and prove it is sound and complete. We show how Maude rewriting system can be used to encode and verify interesting properties of ??OCRS models using existing model checking techniques.

Context-aware Multi-agent System in Machine-to-Machine Communication

Machine-to-Machine (M2M) communication refers to communication among machines without or with only limited human inter- vention. The paper deals with context-awareness in M2M communication and proposes rich presence information as a concept to address context information in a multi-agent system supporting networking of machines. Rich Presence Information Data (RPID) extensions suitable for M2M are introduced into Context-aware Mobile Agent Network model. Case study describing context information related to energy consumption and energy saving is presented. Results based on simulations and measurements in a laboratory test-bed are discussed.

Context-aware multiagent system: Planning home care tasks

Context-aware systems are able to capture information from the context in which they are executed, assign a meaning to the gathered information, and change their behavior accordingly. As a result, the systems can offer services to users according to their individual situation within the context. This article analyzes the important aspects of context-aware computing such as capturing information for context attributes and determining the manner of interacting with users in the environment. Used in conjunction with mobile devices, context-aware systems are specifically used to improve the usability of applications and services. This article proposes the home care context-aware computing (HoCCAC) multiagent system that identifies and maintains a permanent fix on the location of patients in their home, and manages the infrastructure of services within their environment securely and reliably by processing and reasoning the data received. Based on the multiagent system, a prototype was developed to monitor patients in their home. The HoCCAC multiagent system uses a critical path method-based planning model that, in the present study, prepares the most optimal task-planning schedule for the patients in their home, is capable of reacting automatically when faced with dangerous or emergency situations, replanning any plans in progress and sending alert messages to the system. The results obtained with this prototype are presented in this article.

A Context-Aware Multi-Agent System as a Middleware for Ambient Intelligence

There is currently a lot of work in Ambient Intelligence--or AmI--reporting on specific scenarios, or on implementations of particular cases. In the same time, there is a common agreement of the fact that AmI applications should be pervasive, covering a large number of devices, assisting a large number of people, and serving a large number of purposes. In an attempt to achieve scalable scenarios and implementations, we have focused our research on the development of a generic middleware layer for the context-aware transfer and exchange of information between devices. This paper presents a model for a such middleware, based on software agents, in which context-awareness is implemented both in the agent's representation of context information, and in the logical topology of the agent system. The model is oriented towards decentralization of the system and relies mostly on local behavior. The paper also reports on several proof-of-concept applications that have been developed and tested using the proposed model, proving thus the validity of the approach.

IConAwa – An intelligent context-aware system

Context-awareness becomes an increasingly important concept in the development of mobile and ubiquitous systems. Applications and services, which run in these kinds of highly dynamic environments, should be aware of and adapt to their contexts. Context-aware applications improve and enrich people’s interactions with devices, computers and other people.In this paper, design and development of iConAwa, which is an intelligent context-aware multi-agent system proactively providing mobile users with context-aware information and services, is described. In iConAwa, mobile users can get information and services about nearby resources (attraction points) according to their context and also communicate with each other by exchanging messages. Context and point of interest ontologies are developed in OWL. Context and points of interest are modelled in a flexible and extensible way by the developed ontology models. Knowledge sharing and knowledge reuse are also provided by using these ontology models. iConAwa makes use of rule-based context reasoning which provides derivation of high level implicit context from low level explicit context. With this approach context reasoning is decoupled from the source code of the system. JADE agent development framework is used to develop the agents and Jena semantic web framework is used to manipulate ontologies and for rule based reasoning.