Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction

British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

Weighted logics for artificial intelligence : an introductory discussion

International audienceBefore presenting the contents of the special issue, we propose a structured introductory overview of a landscape of the weighted logics (in a general sense) that can be found in the Artificial Intelligence literature, highlighting their fundamental differences and their application areas

Active fuzzy weighting ensemble for dealing with concept drift

© 2018, the Authors. The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by. The existence of different types of concept drift makes it more difficult for learning algorithms to track. This paper proposes a novel adaptive ensemble algorithm, the Active Fuzzy Weighting Ensemble, to handle data streams involving concept drift. During the processing of data instances in the data streams, our algorithm first identifies whether or not a drift occurs. Once a drift is confirmed, it uses data instances accumulated by the drift detection method to create a new base classifier. Then, it applies fuzzy instance weighting and a dynamic voting strategy to organize all the existing base classifiers to construct an ensemble learning model. Experimental evaluations on seven datasets show that our proposed algorithm can shorten the recovery time of accuracy drop when concept drift occurs, adapt to different types of concept drift, and obtain better performance with less computation costs than the other adaptive ensembles

Guiding Cooperative Stakeholders to Compromise Solutions Using an Interactive Tradespace Exploration Process

Engineering projects frequently involve the cooperation of multiple stakeholders with varying objectives and preferences for the resulting system. Finding a mutually agreeable solution is of paramount importance in order to assure the successful completion of these projects, particularly when different stakeholders are splitting the costs because none can afford to finance the project on their own. This paper proposes a process for uncovering potential mutually agreeable solutions between conflicting stakeholders, without relying on hypothetical aggregate or super-stakeholder preferences, by using guided individual preference compromises and efficiency tradeoffs. Opportunities for experimentally testing the process, with results investigating its usability and solution quality, are discussed. Further directions to improve and expand the process are also discussed, with attention paid to the design of the process as it relates to promoting an implied concept of “goodness” or “fairness” of compromise along with the ability of the process to incorporate advanced interactive technology to improve knowledge retention and understanding of the participating stakeholders.Massachusetts Institute of Technology. Systems Engineering Advancement Research Initiativ

Trust networks for recommender systems

Recommender systems use information about their user’s profiles and relationships to suggest items that might be of interest to them. Recommenders that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional systems, provided they succeed in utilizing the additional (dis)trust information to their advantage. Such trust-enhanced recommenders consist of two main components: recommendation technologies and trust metrics (techniques which aim to estimate the trust between two unknown users.) We introduce a new bilattice-based model that considers trust and distrust as two different but dependent components, and study the accompanying trust metrics. Two of their key building blocks are trust propagation and aggregation. If user a wants to form an opinion about an unknown user x, a can contact one of his acquaintances, who can contact another one, etc., until a user is reached who is connected with x (propagation). Since a will often contact several persons, one also needs a mechanism to combine the trust scores that result from several propagation paths (aggregation). We introduce new fuzzy logic propagation operators and focus on the potential of OWA strategies and the effect of knowledge defects. Our experiments demonstrate that propagators that actively incorporate distrust are more accurate than standard approaches, and that new aggregators result in better predictions than purely bilattice-based operators. In the second part of the dissertation, we focus on the application of trust networks in recommender systems. After the introduction of a new detection measure for controversial items, we show that trust-based approaches are more effective than baselines. We also propose a new algorithm that achieves an immediate high coverage while the accuracy remains adequate. Furthermore, we also provide the first experimental study on the potential of distrust in a memory-based collaborative filtering recommendation process. Finally, we also study the user cold start problem; we propose to identify key figures in the network, and to suggest them as possible connection points for newcomers. Our experiments show that it is much more beneficial for a new user to connect to an identified key figure instead of making random connections

Fifty years of fuzzy research: A bibliometric analysis and a long-term comparative overview

This paper presents a general overview and a long-term comparison in fuzzy logic research published between 1965 and 2017, obtained via Web of Science. The paper analyzes the growth, impact, trends and regional localization of fuzzy re-search. Conventional, sophisticated among others bibliometric indicators have applies. It aggregates the information according to different levels and criteria including researchers, publications, institutions, or countries. A global perspective have been provided through comparisons of regional aggregates and compound annual growth rates that strengthen the indicators applied in this article. The results permit to visualize the influence, importance, evolution and performance of the fuzzy research as well its contribution to, and transversality with other fields. The findings show that China continues to be a leader in number of contributions. There has been a recent relative decline in the United States contributions overall. Asian and African contribu-tions to scientific literature have grown noticeably. The results also provide a framework for the use of indicators adjusted to specific contexts and relevant information for future research

Reactivity to sustainability metrics: A configurational study of motivation and capacity

Previous research on reactivity – defined as changing organisational behaviour to better conform to the criteria of measurement in response to being measured – has found significant variation in company responses towards sustainability metrics. We propose that reactivity is driven by dialogue, motivation and capacity in a configurational way. Empirically, we use fuzzy set Qualitative Comparative Analysis (fsQCA) to analyse company responses to the sustainability index FTSE4Good. We find evidence of complimentary and substitute effects between motivation and capacity. Based on these effects we develop a typology of reactivity to sustainability metrics, which also theorises the use of metrics as tools for performance feedback and the building of calculative capacity. We show that when reactivity is studied configurationally, we can identify previously underacknowledged types of responses. We discuss the theoretical and practical implications for studying and using sustainability metrics as governance tools for responsible behaviour