TY - CONF AU - Polevoy, Gleb AU - de Weerdt, M.M. ID - 17653 KW - interaction KW - reciprocation KW - contribute KW - shared effort KW - curbing KW - convergence KW - threshold KW - Nash equilibrium KW - social welfare KW - efficiency KW - price of anarchy KW - price of stability T2 - Proceedings of the 29th Benelux Conference on Artificial Intelligence TI - Reciprocation Effort Games ER - TY - CONF AB - While finding minimum-cost spanning trees (MST) in undirected graphs is solvable in polynomial time, the multi-criteria minimum spanning tree problem (mcMST) is NP-hard. Interestingly, the mcMST problem has not been in focus of evolutionary computation research for a long period of time, although, its relevance for real world problems is easy to see. The available and most notable approaches by Zhou and Gen as well as by Knowles and Corne concentrate on solution encoding and on fairly dated selection mechanisms. In this work, we revisit the mcMST and focus on the mutation operators as exploratory components of evolutionary algorithms neglected so far. We investigate optimal solution characteristics to discuss current mutation strategies, identify shortcomings of these operators, and propose a sub-tree based operator which offers what we term Pareto-beneficial behavior: ensuring convergence and diversity at the same time. The operator is empirically evaluated inside modern standard evolutionary meta-heuristics for multi-criteria optimization and compared to hitherto applied mutation operators in the context of mcMST. AU - Bossek, Jakob AU - Grimme, Christian ID - 48857 KW - Convergence KW - Encoding KW - Euclidean distance KW - Evolutionary computation KW - Heating systems KW - Optimization KW - Standards T2 - 2017 IEEE Symposium Series on Computational Intelligence (SSCI) TI - A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem ER - TY - CONF AU - Polevoy, Gleb AU - de Weerdt, Mathijs AU - Jonker, Catholijn ID - 17656 KW - agent's influence KW - behavior KW - convergence KW - perron-frobenius KW - reciprocal interaction KW - repeated reciprocation SN - 978-1-4503-4239-1 T2 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems TI - The Convergence of Reciprocation ER - TY - CONF AB - In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms. AU - Hoang, Manh Kha AU - Haeb-Umbach, Reinhold ID - 11816 KW - Gaussian processes KW - Global Positioning System KW - convergence KW - expectation-maximisation algorithm KW - fingerprint identification KW - indoor radio KW - signal classification KW - wireless LAN KW - EM algorithm KW - ML estimation KW - WiFi indoor positioning KW - censored Gaussian data classification KW - clipped data KW - convergence properties KW - expectation maximization algorithm KW - fingerprinting method KW - maximum likelihood estimation KW - optimal classification KW - parameters estimation KW - portable devices sensitivity KW - signal strength measurements KW - wireless LAN positioning systems KW - Convergence KW - IEEE 802.11 Standards KW - Maximum likelihood estimation KW - Parameter estimation KW - Position measurement KW - Training KW - Indoor positioning KW - censored data KW - expectation maximization KW - signal strength KW - wireless LAN SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) TI - Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning ER -