Read more
Innovations in Intelligent Machines is a very timely volume that takes a fresh look on the recent attempts of instilling human-like intelligence into computer-controlled devices. By contrast to the machine intelligence research of the last two decades, the recent work in this area recognises explicitly the fact that human intelligence is not purely computational but that it also has an element of empirical validation (interaction with the environment). Also, recent research recognises that human intelligence does not always prevent one from making errors but it equips one with the ability to learn from m- takes. The latter is the basic premise for the development of the collaborative (swarm)intelligencethatdemonstratesthevalueofthevirtualexperiencepool assembled from cases of successful and unsuccessful execution of a particular algorithm. The editors are to be complemented for their vision of designing a fra- work within which they ask some fundamental questions about the nature of intelligence in general and intelligent machines in particular and illustrate answers to these questions with speci?c practical system implementations in the consecutive chapters of the book.
List of contents
Intelligent Machines: An Introduction.- Predicting Operator Capacity for Supervisory Control of Multiple UAVs.- Team, Game, and Negotiation based Intelligent Autonomous UAV Task Allocation for Wide Area Applications.- UAV Path Planning Using Evolutionary Algorithms.- Evolution-based Dynamic Path Planning for Autonomous Vehicles.- Algorithms for Routing Problems Involving UAVs.- State Estimation for Micro Air Vehicles.- Evolutionary Design of a Control Architecture for Soccer-Playing Robots.- Toward Robot Perception through Omnidirectional Vision.
Summary
Innovations in Intelligent Machines is a very timely volume that takes a fresh look on the recent attempts of instilling human-like intelligence into computer-controlled devices. By contrast to the machine intelligence research of the last two decades, the recent work in this area recognises explicitly the fact that human intelligence is not purely computational but that it also has an element of empirical validation (interaction with the environment). Also, recent research recognises that human intelligence does not always prevent one from making errors but it equips one with the ability to learn from m- takes. The latter is the basic premise for the development of the collaborative (swarm)intelligencethatdemonstratesthevalueofthevirtualexperiencepool assembled from cases of successful and unsuccessful execution of a particular algorithm. The editors are to be complemented for their vision of designing a fra- work within which they ask some fundamental questions about the nature of intelligence in general and intelligent machines in particular and illustrate answers to these questions with speci?c practical system implementations in the consecutive chapters of the book.
