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Open multi-agent systems are typically formed from heterogeneous peers operating in a decentralised manner. Hence, their constituent agents must evaluate possible actions and opportunities based on local, subjective knowledge. When agents have insufficient personal experience, they may inevitably rely on their social connections to act as a source of relevant information or recommendations. We describe an agent-mediated electronic market for investigating social interaction within the context of evolving heterogeneous distributed networks. In our scenario, consumers look for appropriate services and this service choice is informed via peer recommendations. We define two alternative algorithms for selecting peers based on perceived similarity and we evaluate them on their ability to organise an overlay network such that it acts as a passive filter, tailoring the information that agents use to select services in the market. We use this scenario to explore the link between the peer selection algorithms and the emergent network topologies, as well as the impact of the peer selection algorithm on the agents’ performance in choosing services based on peer recommendations. Our simulation results demonstrate a qualitative difference in the behaviour of the algorithms, with optimal algorithm selection relying on information regarding the preferences of the wider population of agents. [ABSTRACT FROM AUTHOR]

Web personalisation systems are used to enhance the user experience by providing tailor-made services based on the user’s interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users’ changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalisation system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilises our dynamic user profile to provide a personalised search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours. [ABSTRACT FROM AUTHOR]

Highlights: [•] We introduce a novel method for learning and modelling contextual user profiles. [•] We develop methods for inferring hidden semantic information from user profiles. [•] We introduce an approach to providing contextual recommendation services. [•] We present a domain-independent context-aware personalization system. [ABSTRACT FROM AUTHOR]

Energy conservation is a fundamental requirement in achieving sustainable development, enhancing environmental protection and improving economic and social wellbeing by reducing energy consumption. Carbon emissions are a significant manifestation of energy consumption. Real-time monitoring and forecasting of air quality plays a crucial role in effectively controlling carbon emissions and improving energy conservation measures. Complex Event Processing (CEP) is a technical framework for streaming data processing based on predefined rules and is widely used for real-time detection. Yet as the diversity of data increases, it becomes exceptionally difficult for experts to formulate the CEP rules. Rule-based algorithms can address this issue to a certain extent. However, the CEP rules extracted through these methods are obtained from static data and are often simple and independent of each other. Here, we regard such rules as direct rules, which ignore possible connections between events that usually imply deeper rules. In contrast, we refer to rules that reflect interconnections between events and reveal higher-level principles as indirect rules. In this paper, we propose a methodology fusing interpretable machine learning and decision mining to extract direct and indirect multi-level CEP rules from air pollution datasets for real-time detection, prediction, and early warning. First, we utilize the decision tree algorithm to extract direct rules to detect air quality in real-time. Next, we utilize the process mining algorithm to model the changes in air states and extract the Petri net model from it that reflects the changes in air quality. Then, we utilize our proposed decision mining algorithm to extract indirect CEP rules. Indirect rules can help to understand how pollutants in the air change over time. In addition, they can reveal underlying trends and patterns of air quality change for prediction and early warning. Finally, we validate the accuracy and effectiveness of our approach using a real air pollution dataset. This work contributes to the field of air pollution detection and can help promote the optimization of energy conservation, while it can also support the development of regulatory strategies. • A multi-level CEP rules extraction method is proposed for air quality detection. • A novel method combining machine learning and process mining to extract CEP rules. • A novel way to analyze decision points in Petri nets to monitor air states changes. [ABSTRACT FROM AUTHOR]

Highlights: [•] We explore the problems derived from aggregating quantitative opinions in reputation. [•] We propose a mechanism that aggregates opinions based on pairwise comparisons. [•] We show the suitability of the mechanism with an evaluation on real data sets. [ABSTRACT FROM AUTHOR]

The rapid growth of data in the pharmaceutical area has created new challenges for large-scale data mining like Drug-Drug Interaction (DDI) analysis. To meet these challenges, various types of data related to DDI must be integrated with true semantics. However, the existing tools do not provide automated DDI analysis. Interaction details are not machine readable and pharmacists need to do further processing for its extraction. This research paper proposed an ontology-driven Advanced Drug-Drug Interaction (ADDI) system to assists the physicians and pharmacists to identify the DDI effects. ADDI provides ontological definitions and semantic relations among diseases, drugs, ingredients, action mechanism, physiologic effect, dosage formation, administration methods, DDI mechanism, DDI types (Antagonism, Synergism, Potentiation, and Interaction with metabolism), DDI reactions, their frequency and duration. It can be used as Semantic Information Layer (SIL) to resolve the heterogeneity problem and can play a significant role to remove the barriers for semantic interoperability. [ABSTRACT FROM AUTHOR]