Dynamic reconfiguration of multi-agent systems based on autonomy oriented computing

Dynamic reconfiguration has been listed as one of the key challenges in support of agent adaptation to environments, which has attracted much attention of researchers world wide. To tackle this tough problem, an agent-based dynamic reconfiguration model (ADRM) is proposed from the autonomy-oriented computing (AOC) point of view. The ERA (environment-reactive rules-agents) algorithm used in AOC is improved to support the organization formation behavior, which is essential in dynamic reconfiguration. To test the efficiency of this model and the effectiveness of different reactive behaviors, the performance of this model was investigated under different selection probabilities. <br /

Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

Prediction Markets: A Systematic Review and Meta-Analysis

Prediction markets (PM) have drawn considerable attention in recent years as a tool for forecasting events. Studies surveying and examining relevant the trends of PM using traditional approaches have been reported in the literature. However, research using meta-analysis to review Prediction markets systems is very limited in Management Information System (MIS). This paper aimed to fill this gap by using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to study Prediction markets trends over the past decades. Our results are as follows. First, we find that shows that more than 64% of academic studies on Prediction markets are published in top journals such as Journal of the Association for Information Systems, Journal of Consumer Research and Information Systems Research. Second, we showed that Prediction markets applications can be can be divided into two groups: internal use PMS and general public usage. Finally, our significant meta-analysis result show that on average prediction markets is 79% more accurate than alternative forecast methods based

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

Adaptive human motion analysis and prediction

Human motion analysis and prediction is an active research area where predicting human motion is often performed for a single time step based on historical motion. In recent years, longer term human motion prediction has been attempted over a number of future time steps. Most current methods learn motion patterns (MPs) from observed trajectories and then use them for prediction. However, these learned MPs may not be indicative due to inadequate observation, which naturally affects the reliability of motion prediction. In this paper, we present an adaptive human motion analysis and prediction method. It adaptively predicts motion based on the classified MPs in terms of their credibility, which refers to how indicative the learned MPs are for the specific environment. The main contributions of the proposed method are as follows: First, it provides a comprehensive description of MPs including not only the learned MPs but also their evaluated credibility. Second, it predicts long-term future motion with reasonable accuracy. A number of experiments have been conducted in simulated scenes and real-world scenes and the prediction results have been quantitatively evaluated. The results show that the proposed method is effective and superior in its performance when compared with a recursively applied Auto-Regressive (AR) model, which is called the Recursive Short-term Predictor (RSP) for long-term prediction. The proposed method has 17.73% of improvement over the RSP in prediction accuracy in the experiment with the best performance. On average, the proposed method has 5% improvement over the RSP in prediction accuracy over 10 experiments. © 2011 Elsevier Ltd. All rights reserved.postprin

THOR: A Hybrid Recommender System for the Personalized Travel Experience

One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected

Parametric Protocol-Driven Agents and their Integration in JADE

Abstract. In this paper we introduce &quot;Template Global Types&quot; which extend Constrained Global Types to support a more generic and modular approach to define protocols, meant as patterns of events of a given type. Protocols can be used both for monitoring the behavior of distributed computational entities and for driving it. In this paper we show the potential of Template Global Types in the domain of protocol-driven intelligent software agents. The interpreter for &quot;executing&quot; Template Global Types has a very natural implementation in Prolog which can easily implement the transition rules for moving from one state to another one, given that an event has been perceived (in case of monitoring) or generated for execution (in case of protocol-driven behavior). This interpreter has been integrated into the Jason logic-based agent framework with limited effort, thanks to the native support that Jason offers to Prolog. In order to demonstrate the flexibility and portability of our approach, which goes beyond the boundaries of logic-based frameworks, in this paper we discuss the integration of the protocol-driven interpreter into the JADE agent framework, entirely implemented in Java