Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The Art and Science of Negotiation

https://hal.archives-ouvertes.fr/hal-01436218/document

Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake.

This paper analyzes trust and power as means of co-ordinating trans-organizational relationships. It is argued that, depending on the institutional environment, there are two distinct patterns of controlling relationships, where trust and power are interrelated in quite different ways. First, both mechanisms are generated at the inter-personal level and either trust or power dominates the relationship. Second, power occurs at the level of the structural framework of relationships and is highly conducive to developing trust between individual organizations. Thus, specific forms of trust and power are identified and the institutional environment is viewed as playing a crucial role in shaping the quality of trans-organizational relations. The theoretical background of the paper mainly draws on conceptual ideas of Systems Theory, Structuration Theory and New Institutionalism

Trust, Power and Control in Trans-Organizational Relations. Working Paper WP 129 (6/99)

EvoCOMNET Contributions.- New Research in Nature Inspired Algorithms for Mobility Management in GSM Networks.- Adaptive Local Search for a New Military Frequency Hopping Planning Problem.- SS vs PBIL to Solve a Real-World Frequency Assignment Problem in GSM Networks.- Reconstruction of Networks from Their Betweenness Centrality.- A Self-learning Optimization Technique for Topology Design of Computer Networks.- A Comparative Study of Fuzzy Inference Systems, Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Portscan Detection.- EvoFIN Contributions.- Evolutionary Single-Position Automated Trading.- Genetic Programming in Statistical Arbitrage.- Evolutionary System for Generating Investment Strategies.- Horizontal Generalization Properties of Fuzzy Rule-Based Trading Models.- Particle Swarm Optimization for Tackling Continuous Review Inventory Models.- Option Model Calibration Using a Bacterial Foraging Optimization Algorithm.- A SOM and GP Tool for Reducing the Dimensionality of a Financial Distress Prediction Problem.- Quantum-Inspired Evolutionary Algorithms for Financial Data Analysis.- EvoHOT Contributions.- Analysis of Reconfigurable Logic Blocks for Evolvable Digital Architectures.- Analogue Circuit Control through Gene Expression.- Discovering Several Robot Behaviors through Speciation.- Architecture Performance Prediction Using Evolutionary Artificial Neural Networks.- Evolving a Vision-Driven Robot Controller for Real-World Indoor Navigation.- Evolving an Automatic Defect Classification Tool.- Deterministic Test Pattern Generator Design.- An Evolutionary Methodology for Test Generation for Peripheral Cores Via Dynamic FSM Extraction.- Exploiting MOEA to Automatically Geneate Test Programs for Path-Delay Faults in Microprocessors.- EvoIASP Contributions.- Evolutionary Object Detection by Means of Naive Bayes Models Estimation.- An Evolutionary Framework for Colorimetric Characterization of Scanners.- Artificial Creatures for Object Tracking and Segmentation.- Automatic Recognition of Hand Gestures with Differential Evolution.- Optimizing Computed Tomographic Angiography Image Segmentation Using Fitness Based Partitioning.- A GA-Based Feature Selection Algorithm for Remote Sensing Images.- An Evolutionary Approach for Ontology Driven Image Interpretation.- Hybrid Genetic Algorithm Based on Gene Fragment Competition for Polyphonic Music Transcription.- Classification of Seafloor Habitats Using Genetic Programming.- Selecting Local Region Descriptors with a Genetic Algorithm for Real-World Place Recognition.- Object Detection Using Neural Networks and Genetic Programming.- Direct 3D Metric Reconstruction from Multiple Views Using Differential Evolution.- Discrete Tomography Reconstruction through a New Memetic Algorithm.- A Fuzzy Hybrid Method for Image Decomposition Problem.- Triangulation Using Differential Evolution.- Fast Multi-template Matching Using a Particle Swarm Optimization Algorithm for PCB Inspection.- EvoMUSART Contributions.- A Generative Representation for the Evolution of Jazz Solos.- Automatic Invention of Fitness Functions with Application to Scene Generation.- Manipulating Artificial Ecosystems.- Evolved Diffusion Limited Aggregation Compositions.- Scaffolding for Interactively Evolving Novel Drum Tracks for Existing Songs.- AtomSwarm: A Framework for Swarm Improvisation.- Using DNA to Generate 3D Organic Art Forms.- Towards Music Fitness Evaluation with the Hierarchical SOM.- Evolutionary Pointillist Modules: Evolving Assemblages of 3D Objects.- An Artificial-Chemistry Approach to Generating Polyphonic Musical Phrases.- Implicit Fitness Functions for Evolving a Drawing Robot.- Free Flight in Parameter Space: A Dynamic Mapping Strategy for Expressive Free Impro.- Modelling Video Games' Landscapes by Means of Genetic Terrain Programming - A New Approach for Improving Users' Experience.- Virtual Constructive Swarm Compositions and Inspirations.- New-Generation Methods in an Interpolating EC Synthesizer Interface.- Composing Music with Neural Networks and Probabilistic Finite-State Machines.- TransFormer #13: Exploration and Adaptation of Evolution Expressed in a Dynamic Sculpture.- EvoNUM Contributions.- Multiobjective Tuning of Robust PID Controllers Using Evolutionary Algorithms.- Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress in High-Dimensional Spaces.- Scalable Continuous Multiobjective Optimization with a Neural Network-Based Estimation of Distribution Algorithm.- Cumulative Step Length Adaptation for Evolution Strategies Using Negative Recombination Weights.- Computing Surrogate Constraints for Multidimensional Knapsack Problems Using Evolution Strategies.- A Critical Assessment of Some Variants of Particle Swarm Optimization.- An Evolutionary Game-Theoretical Approach to Particle Swarm Optimisation.- A Hybrid Particle Swarm Optimization Algorithm for Function Optimization.- EvoSTOC Contributions.- Memory Based on Abstraction for Dynamic Fitness Functions.- A Memory Enhanced Evolutionary Algorithm for Dynamic Scheduling Problems.- Compound Particle Swarm Optimization in Dynamic Environments.- An Evolutionary Algorithm for Adaptive Online Services in Dynamic Environment.- EvoTHEORY Contributions.- A Study of Some Implications of the No Free Lunch Theorem.- Negative Slope Coefficient and the Difficulty of Random 3-SAT Instances.- EvoTRANSLOG Contributions.- A Memetic Algorithm for the Team Orienteering Problem.- Decentralized Evolutionary Optimization Approach to the p-Median Problem.- Genetic Computation of Road Network Design and Pricing Stackelberg Games with Multi-class Users.- Constrained Local Search Method for Bus Fleet Scheduling Problem with Multi-depot with Line Change.- Evolutionary System with Precedence Constraints for Ore Harbor Schedule Optimization.

Applications of evolutionary computing: EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings.

Electronic Commerce technology has changed the way traditional business is being done. Transactions' complexity is increased due both to the huge amount of available information and also to the environment dynamics. Moreover, Electronic Commerce has enabled the arising of new economical structures, as it is the case of Virtual Organisations.

Our research aims at providing flexible and general-purpose systems for intelligent negotiation, both for Electronic Commerce and Virtual Organisation formation.

This paper proposes an Electronic Market architecture implemented through a Multi-Agent system. This architecture includes both a specific market agent which plays the role of market coordinator, as well as agents representing the individual business partners with their own goals and strategies. We also include a sophisticated negotiation protocol through multi-criteria and distributed constraint formalisms. An online, continuous reinforcement learning algorithm has been designed to enable agents to adapt themselves according to the changing environment, including the competitor agents.

Agents Advanced Features for Negotiation in Electronic Commerce and Virtual Organisations Formation Processes

Economic organisations are facing new challenges due to the proliferation of Electronic Markets where competition and responsiveness are issues that will determine organisation’s economic survival. A Virtual Enterprise is a temporary network of individual enterprises, which, due to its higher flexibility and agility is capable of effectively responding to those challenging requirements. This paper proposes an Electronic Market system’s architecture together with a multi-criteria negotiation protocol for Virtual Enterprise formation.

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An Electronic Market Architecture for the Formation of Virtual Enterprises

Computational Trust and Reputation (CTR) systems are platforms capable of collecting trust information about candidate partners and of computing confidence scores for each one of these partners. These systems start to be viewed as vital elements in environments of electronic institutions, as they support fundamental decision making processes, such as the selection of business partners and the automatic and adaptive creation of contractual terms and associated enforcement methodologies. In this article, we propose a model for the aggregation of trust evidences that computes confidence scores taking into account dynamic properties of trust. We compare our model with a traditional statistical model that uses weighted means to compute trust, and show experimental results that show that in certain scenarios the consideration of the trust dynamics allows for a better estimation of confidence scores.

/pdf/computing-confidence-values-does-trust-dynamics-matter-3xlt0nyq78.pdf

Computing Confidence Values: Does Trust Dynamics Matter?

Electronic commerce competitiveness, due to market openness and dynamics, enabled the arising of new organizational structures, as it is the case with virtual enterprises. The virtual enterprise (VE) concept can effectively answer to new demanding market requirements, as it combines the core competencies of independent and heterogeneous enterprises that collaborate in a temporary and loosely linked network, thereby presenting high flexibility and agility. However, institutional and social laws must be introduced here to enforce and regulate individual enterprises’ behavior. An electronic institution is a framework that enables through a communication network automatic transactions between electronic business parties, according to sets of explicit institutional norms and rules. This chapter presents and discusses tools for automatic negotiation and operation monitoring that make an electronic institution a suitable framework for helping in the two most important stages of a VE’s life cycle: formation and operation. Moreover, the electronic contract concept is defined and discussed. 228 Rocha, Cardoso, & Oliveira Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. INTRODUCTION The growth of information and communication technology has changed the way traditional commerce has been done by eliminating time and space restrictions. A new way of commerce, based on network communications, encompasses two fields: the business-to-consumer (B2C) and the business-to-business (B2B) electronic commerce. In the B2C electronic commerce, business participants are individual buyers and sellers that announce and negotiate over a final product or service. In the B2B electronic commerce, contrary to what happens in B2C, the goal of the business transaction is not a final product, and generally, business participants are enterprises that need to include in their own processes products that are outside of their expertise domain or resources they do not own. The work reported here is related to the last-mentioned type of electronic commerce, that is, the B2B electronic commerce. The electronic commerce has increased the business competitiveness, due both to the market openness and dynamics. Enterprises try to answer these new market requirements by engaging themselves in temporary corporations, thereby presenting a flexible structure that changes dynamically according to current market situations. This new agile organizational structure is called VE. All those enterprises collaborate for a global goal with their competencies, knowledge, and resources. Agility is possible, because individual enterprises that belong to the VE are loosely coupled in this networked structure, and, although working for the VE global goal, enterprises maintain their autonomy. A computing platform named ForEV (acronym for Virtual Enterprises Formation equivalent in Portuguese) was developed for supporting the VE formation stage. The VE formation stage has as its primary objective the creation of an organization able to compete as well as respond to the demanding requirements coming from an open market, by including in that organization those enterprises that have either the higher competence or present the best transaction conditions for that business opportunity. Our approach includes an iterative, adaptive, multiattribute negotiation protocol using qualitative argumentation (the “Q-negotiation” algorithm). The negotiation that takes place during the VE formation stage leads to the agreement of an electronic contract that should be signed by all individual enterprises selected as partners in the VE. The VE operation stage uses this electronic contract to monitor the VE activity. The electronic contract describes the rights and duties of all VE partners, as well as penalties to apply to those that do not satisfy the agreement. The rationale of this chapter includes the understanding of the VE concept, a definition of a generic model of an electronic institution, our proposal of tools enabling the electronic institution’s role in helping in the VE formation stage, the exploitation of electronic-contracting services within an electronic institution that helps in the VE operation stage, and conclusions and directions for future work.

/pdf/contributions-to-an-electronic-institution-supporting-4f98uq0sj9.pdf

Contributions to an Electronic Institution supporting Virtual Enterprises' Life cycle

Electronic transactions are of increasing use due to its openness and continuous availability. The rapid growth of information and communication technologies has helped the expansion of these electronic transactions, however, issues related to security and trust are yet limiting its action space, mainly in what concerns business to business activity. This paper introduces an Electronic Institution framework to help in electronic transactions management making available norms and rules as well as monitoring business participants' behaviour in specific electronic business transactions. Virtual Organisation (VO) life cycle has been used as a complex scenario encompassing electronic transactions and where Electronic Institutions help in both formation and operation phase. A flexible negotiation process that includes multi-attribute and learning capabilities as well as distributed dependencies resolution is here proposed for VO formation. "Phased commitment" is another concept here introduced for VO operation monitoring through the Electronic Institution.

Ana Paula Rocha

Papers

Agents Advanced Features for Negotiation in Electronic Commerce and Virtual Organisations Formation Processes

An Electronic Market Architecture for the Formation of Virtual Enterprises

Computing Confidence Values: Does Trust Dynamics Matter?

Contributions to an Electronic Institution supporting Virtual Enterprises' Life cycle

Electronic Institutions as a Framework for Agents' Negotiation and Mutual Commitment