AI planning is a broad research topic, linked with such issues as robotics, control theory, operations research and learning. The purpose of EWSP '93 was twofold. Planning under certainty, or classical search-based planning is one direction in the submitted papers, with approaches ranging from the introduction of conditional actions to methods based on statistics and decision theory.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data

This book constitutes the thoroughly refereed post-proceedings of the 5th European Conference on Planning, ECP'99, held in Durham, UK, in September 1999. The 27 revised full papers presented together with one invited survey were carefully reviewed and selected for inclusion in the book. They address all current aspects of AI planning and scheduling. Several prominent planning paradigms are represented, including planning as satisfiability and other model checking strategies, planning as heuristic state-space search, and Graph-plan-based approaches. Moreover, various new scheduling approaches and combinations of planning and scheduling methods are introduced.

In recent years, autonomous robots, including Xavier, Martha [1], Rhino [2,3], Minerva,and Remote Agent, have shown impressive performance in long-term demonstrations. In NASA’s Deep Space program, for example, an - tonomous spacecraft controller, called the Remote Agent [5], has autonomously performed a scienti?c experiment in space. At Carnegie Mellon University, Xavier [6], another autonomous mobile robot, navigated through an o?ce - vironment for more than a year, allowing people to issue navigation commands and monitor their execution via the Internet. In 1998, Minerva [7] acted for 13 days as a museum tourguide in the Smithsonian Museum, and led several thousand people through an exhibition. These autonomous robots have in common that they rely on plan-based c- trol in order to achieve better problem-solving competence. In the plan-based approach, robots generate control actions by maintaining and executing a plan that is e?ective and has a high expected utility with respect to the robots’ c- rent goals and beliefs. Plans are robot control programs that a robot can not only execute but also reason about and manipulate [4]. Thus, a plan-based c- troller is able to manage and adapt the robot’s intended course of action — the plan — while executing it and can thereby better achieve complex and changing tasks.

Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. Plan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of applications including: - personal agent assistants - computer and network security - opponent modeling in games and simulation systems - coordination in robots and software agents - web e-commerce and collaborative filtering - dialog modeling - video surveillance - smart homes In this book, follow the history of this research area and witness exciting new developments in the field made possible by improved sensors, increased computational power, and new application areas. - Combines basic theory on algorithms for plan/activity recognition along with results from recent workshops and seminars - Explains how to interpret and recognize plans and activities from sensor data - Provides valuable background knowledge and assembles key concepts into one guide for researchers or students studying these disciplines

Computer systems based on the notion of the computer as assistant have recently become the focus of intense interest. The expanding role of the computer in everyday life and the growing number of untrained users make it necessary to think about new ways of dividing labor between humans and machines. Future systems must take on more tasks and perform them more competently and autonomously than existing systems. If they are to be adequately flexible and responsive to complexity, they cannot automate their performance completely. The aim of designers should be to create computer systems with capabilities similar to those of good assistants in the real world. Effective assistance has many characteristics. An assistant is expected to be competent in some domains of expertise, to know the limits of his/her knowledge, to be able to process inexact instructions from clients, to adjust to and learn from them, to explain his/her behavior and suggestions, and to support clients in communication and cooperation with other people. This book believes that such capabilities can be built into computer systems. To that end, the chapter contributors discuss the concepts and methods--particularly from the fields of artificial intelligence and computer-supported cooperative work (CSCW)--that they have drawn from to develop successful system prototypes. They present several of these prototypes including assistants for graphics design, knowledge discovery in data bases, coordination support, organizational memory, user interface design, and knowledge base construction. As such, this volume helps map out the future for all those involved in computer systems design.