[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

Multiagent systems are made up of multiple interacting intelligent agents -- computational entities to some degree autonomous and able to cooperate, compete, communicate, act flexibly, and exercise control over their behavior within the frame of their objectives They are the enabling technology for a wide range of advanced applications relying on distributed and parallel processing of data, information, and knowledge relevant in domains ranging from industrial manufacturing to e-commerce to health care This book offers a state-of-the-art introduction to multiagent systems, covering the field in both breadth and depth, and treating both theory and practice It is suitable for classroom use or independent study This second edition has been completely revised, capturing the tremendous developments in multiagent systems since the first edition appeared in 1999 Sixteen of the book's seventeen chapters were written for this edition; all chapters are by leaders in the field, with each author contributing to the broad base of knowledge and experience on which the book rests The book covers basic concepts of computational agency from the perspective of both individual agents and agent organizations; communication among agents; coordination among agents; distributed cognition; development and engineering of multiagent systems; and background knowledge in logics and game theory Each chapter includes references, many illustrations and examples, and exercises of varying degrees of difficulty The chapters and the overall book are designed to be self-contained and understandable without additional material Supplemental resources are available on the book's Web site Contributors:Rafael Bordini, Felix Brandt, Amit Chopra, Vincent Conitzer, Virginia Dignum, Jurgen Dix, Ed Durfee, Edith Elkind, Ulle Endriss, Alessandro Farinelli, Shaheen Fatima, Michael Fisher, Nicholas R Jennings, Kevin Leyton-Brown, Evangelos Markakis, Lin Padgham, Julian Padget, Iyad Rahwan, Talal Rahwan, Alex Rogers, Jordi Sabater-Mir, Yoav Shoham, Munindar P Singh, Kagan Tumer, Karl Tuyls, Wiebe van der Hoek, Laurent Vercouter, Meritxell Vinyals, Michael Winikoff, Michael Wooldridge, Shlomo Zilberstein

Multiagent Systems

Exploring and identifying structure is even more important for multivariate data than univariate data, given the difficulties in graphically presenting multivariate data and the comparative lack of parametric models to represent it. Unfortunately, such exploration is also inherently more difficult.

Multivariate Density Estimation

During the last five years, several methods have been proposed for handling nonlinear constraints using evolutionary algorithms (EAs) for numerical optimization problems. Recent survey papers classify these methods into four categories: preservation of feasibility, penalty functions, searching for feasibility, and other hybrids.

In this paper we investigate a new approach for solving constrained numerical optimization problems which incorporates a homomorphous mapping between n-dimensional cube and a feasible search space. This approach constitutes an example of the fifth decoder-based category of constraint handling techniques. We demonstrate the power of this new approach on several test cases and discuss its further potential.

/pdf/evolutionary-algorithms-homomorphous-mappings-and-51texd2af0.pdf

Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization

From the Publisher:
This monograph-like anthology is the first systematic introduction to software agents and their application to future communications systems. Fifteen coherently written chapters by leading software agent researchers provide complementary coverage of the relevant issues. Multi-agent systems and mobile agent approaches are presented, in a well-balanced way and applied to the most important topics in future communications systems. In addition, the volume editors have provided detailed introductory survey chapters.

Software Agents for Future Communication Systems

This paper will show how the accuracy and security of SCADA systems can be improved by using anomaly detection to identify bad values caused by attacks and faults. The performance of invariant induction and n- gram anomaly-detectors will be compared and this paper will also outline plans for taking this work further by integrating the output from several anomaly- detecting techniques using Bayesian networks. Although the methods outlined in this paper are illustrated using the data from an electricity network, this research springs from a more general attempt to improve the security and dependability of SCADA systems using anomaly detection.

/pdf/safeguarding-scada-systems-with-anomaly-detection-59ek4t0m7o.pdf

Safeguarding SCADA Systems with Anomaly Detection

A method of controlling radio resources in a cellular wireless network comprising a plurality of fixed antennas providing overlapping radio coverage, the method comprising dynamically adjusting the radiation patterns of the antennas using co-ordinated distributed control to optimise capacity. The wireless network may be a cellular mobile network or part thereof comprising a plurality of cells, each cell having a base station comprising an antenna, and the method may then further comprise determining the concentration of mobile stations in each cell and changing the radiation/reception pattern of the antennas of some or all of the base stations in response to the concentration of mobile stations in each cell.

Intelligent control of radio resources in a wireless network

Indoor navigation in physical retail type spaces aids the navigation of users to find physical items at known destinations. WiFi Fingerprinting using a mobile phone is perhaps the most widely used method. However, this is power-hungry, and its typical positioning accuracy (2.0 to 3.0 meters) is not enough to differentiate between adjacent narrow aisles to locate items. In this paper, we present a novel (Bluetooth Low Energy) BLE Received Signal Strength Indication (RSSI) ranking based fingerprinting method that uses Kendall Tau Correlation Coefficient (KTCC) to correlate a new signal position with the signal strength ranking of multiple low-power iBeacon devices situated in a retail space. This offers a higher positioning accuracy and is supported in recent smartphones. An important source of error is the RSSI, which varies depending on the model and orientation of the phone. We present a novel way to mitigate this. We validated our method in a retail-like indoor space of Queen Mary Library. We achieved an average positioning error of 0.87 meters which was sufficient to differentiate between physical space aisles.

A BLE RSSI ranking based indoor positioning system for generic smartphones

A cooperative negotiation approach for the real-time control of cellular network coverage is described. The performance of the whole cellular network is improved by contracting and shaping the antenna radiation pattern around traffic "hot spots" and expanding adjacent cell coverage to fill in the coverage loss. The paper shows that the local area real time cooperative negotiation between base stations leads to a near global optimal coverage agreement which is reached in the context of the whole cellular network. Results showing the advantage of this technique are presented. Global optimization using constrained real-coded genetic algorithms provides the benchmark. In our work, instead of using a formal negotiation model of alternating offers, we create certain number of possible local hypotheses and start negotiations based on them. Some negotiation may reach agreements before their deadlines, and the system commits to the best agreement found at the end. This approach is more predictable and controllable than the formal negotiation model. Architecturally the negotiation component described is part of the planning layer of an agent system for resource management in 3G wireless networks. This has a layered architecture with both planning and reactive components. The results are allowing the development of a novel geographic load-balancing scheme for cellular networks that intelligently changes cellular coverage according to the geographic traffic distribution in real time.

John Bigham

Papers

Software Agents for Future Communication Systems

Safeguarding SCADA Systems with Anomaly Detection

Intelligent control of radio resources in a wireless network

A BLE RSSI ranking based indoor positioning system for generic smartphones

Cooperative negotiation in a multi-agent system for real-time load balancing of a mobile cellular network