
Case-Based Reasoning by Beatriz Whiteson
Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.-
Graph Representation Learning
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A Concise Introduction to Models and Methods for Automated Planning
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Learning with Support Vector Machines
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Reasoning with Probabilistic and Deterministic Graphical Models
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Action Programming Languages
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Human Computation
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Learning and Decision-Making from Rank Data
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Explainable Human-AI Interaction
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Network Embedding
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Predicting Human Decision-Making
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Game Theory for Data Science
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Strategic Voting
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Statistical Relational Artificial Intelligence
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A Short Introduction to Preferences
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Representing and Reasoning with Qualitative Preferences
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Robot Learning from Human Teachers
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Visual Object Recognition
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General Game Playing
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Essential Principles for Autonomous Robotics
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Federated Learning
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Graph-Based Semi-Supervised Learning
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An Introduction to Constraint-Based Temporal Reasoning
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Intelligent Autonomous Robotics
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Answer Set Solving in Practice
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Representation Discovery using Harmonic Analysis
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Representations and Techniques for 3D Object Recognition and Scene Interpretation
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Introduction to Intelligent Systems in Traffic and Transportation
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Introduction to Symbolic Plan and Goal Recognition
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Judgment Aggregation
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Metric Learning
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Data Integration
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Trading Agents
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Introduction to Semi-Supervised Learning
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Transfer Learning for Multiagent Reinforcement Learning Systems
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Introduction to Graph Neural Networks
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Introduction to Logic Programming
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An Introduction to the Planning Domain Definition Language
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Lifelong Machine Learning, Second Edition
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Adversarial Machine Learning
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Multi-Objective Decision Making
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Active Learning
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Planning with Markov Decision Processes
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Computational Aspects of Cooperative Game Theory
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Algorithms for Reinforcement Learning
Beatriz Lopez graduated in Computer Science from the Autonomous University of Barcelona in 1986. Two years later she joined the Artificial Intelligence Research Group of the Spanish Scientific Research Council where she received her Ph.D. in Computer Science from the Technical University of Catalonia in 1993 for the work ""Case-base reasoning of strategic plans."" From that point on, her research interest has always been around case-based reasoning and planning and scheduling, now including optimization and learning in distributed environments. She was associate professor from 1992-1995 and 1998-2000 at the Rovira Virgili University and has served as a Computer Science Engineer in several private companies. In February 2011, she co-founded Newronia, a spin-off company from the University of Girona. Since 2000, she has been a senior lecturer in the Department of Electronics, Electricity, and Automation Engineering at the University of Girona. Taught courses include Artificial Intelligence and Machine Learning, in which case-based reasoning is embedded. She is member of the Catalan Association for Artificial Intelligence (member of ECCAI) and several scientific committees.
| SKU | Unavailable |
| ISBN 13 | 9783031004346 |
| ISBN 10 | 3031004345 |
| Title | Case-Based Reasoning |
| Author | Beatriz Whiteson |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2013-05-01 |
| Number of pages | 87 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |












































