A Self-adaptive Module for Cross-understanding in Heterogeneous MultiAgent Systems

We propose a self-adaptive module, called LUDA (Learning Usefulness of DAta) to tackle the problem of cross-understanding in heterogeneous multiagent systems. In this work heterogeneity concerns the agents usage of information available under different reference frames. Our goal is to enable an agent to understand other agents information. To do this, we have built the LUDA module analysing redundant information to improve their accuracy. The closest domains addressing this problem are feature selection and data imputation. Our module is based on the relevant characteristics of these two domains, such as selecting a subset of relevant information and estimating the missing data value. Experiments are conducted using a large variety of synthetic datasets and a smart city real dataset to show the feasibility in a real scenario. The results show an accurate transformation of other information, an improvement of the information use and relevant computation time for agents decision making.

[1]  Jean-Michel Hoc,et al.  Role of a Common Frame of Reference in Cognitive Cooperation: Sharing Tasks between Agents in Air Traffic Control , 2002, Cognition, Technology & Work.

[2]  Tshilidzi Marwala,et al.  Computational Intelligence for Missing Data Imputation, Estimation, and Management - Knowledge Optimization Techniques , 2009, Computational Intelligence for Missing Data Imputation, Estimation, and Management.

[3]  Ingram Olkin,et al.  An Introduction to Ranking and Selection , 1979 .

[4]  Warren B. Powell,et al.  Ranking and selection meets robust optimization , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[5]  B. Arms,et al.  Cooperation , 1926, Becoming Rooted.

[6]  Manoranjan Parida,et al.  Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network , 2013 .

[7]  Seng W. Loke,et al.  Cooperative Automated Vehicles: A Review of Opportunities and Challenges in Socially Intelligent Vehicles Beyond Networking , 2017, IEEE Transactions on Intelligent Vehicles.

[8]  Bradley Efron,et al.  Missing Data, Imputation, and the Bootstrap , 1994 .

[9]  Kan-Jian Zhang,et al.  Wind power prediction with missing data using Gaussian process regression and multiple imputation , 2018, Appl. Soft Comput..

[10]  Stef van Buuren,et al.  Flexible Imputation of Missing Data , 2012 .

[11]  Jeff Heaton,et al.  Introduction to Neural Networks for C#, 2nd Edition , 2008 .

[12]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[13]  Salima Hassas,et al.  Self-organisation: Paradigms and applications , 2003 .

[14]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[15]  Huan Liu,et al.  Multi-Source Feature Selection via Geometry-Dependent Covariance Analysis , 2008, FSDM.

[16]  J. Brian Gray,et al.  Introduction to Linear Regression Analysis , 2002, Technometrics.

[17]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[18]  Feiping Nie,et al.  Multi-View Clustering and Feature Learning via Structured Sparsity , 2013, ICML.

[19]  Heaton T. Jeff,et al.  Introduction to Neural Networks with Java , 2005 .

[20]  Le Zhang,et al.  A survey of randomized algorithms for training neural networks , 2016, Inf. Sci..