This book combines both learning and planning methodologies and their applications in different domains. It is divided into two parts. The first part contains seven chapters on the ongoing research work in symbolic and connectionist learning. The second part includes seven chapters which provide the current research efforts in planning methodologies and their application to robotics.

Contents:

• An Introduction to Learning and Planning (N G Bourbakis)
• Embedding Learning in a General Frame-Based Architecture (T Tanaka & T M Mitchell)
• Connectionist Learning with CHEBYCHEV Networks and Analysis of its Internal Representation (A Namatame)
• Layered Inductive Learning Algorithms and their Computational Aspects (H Madala)
• An Approach to Combining Explanation-Based and Neural Learning Algorithms (J W Savlick & G G Towell)
• The Application of Symbolic Inductive Learning to the Acquisition and Recognition of Noisy Texture Concepts (P W Pachowicz)
• Automating Technology Adaptation in Design Synthesis (J R Kipps & D D Gajski)
• Connectionist Production Systems in Local and Hierarchical Representation (A Sohn & J L Gaudiot)
• A Parallel Architecture for AI Non-Linear Planning (S Lee & K Chung)
• Heuristic Tree Search Using Nonparametric Statistical Inference Methods (W Zhang & N S V Rao)
• An A* Approach to Robust Plan Recognition for Intelligent Interfaces (R J Calistri-Yeh)
• Differential A*: An Adaptive Search Method Illustrated with Robot Path Planning for Moving Obstacles and Goals and an Uncertain Environment (K I Trovato)
• Path Planning Under Uncertainty (F Yegenoglu & H E Stephanou)
• Knowledge-Based Acquisition in Real-Time Path Planning in Unknown Space (N G Bourbakis)
• Path Planning for Two Cooperating Robot Manipulators (Q Xue & P C Y Sheu)