Computational Collective IntelligenceIntroducing a groundbreaking approach to understanding, measuring, and applying Collective Intelligence Does Collective Intelligence (CI) exist and, if so, how can it be characterized, quantified, and harnessed? Questions such as these continue to be hotly debated within both the scientific and philosophical communities. Yet few researchers working in the fields of artificial intelligence or distributed computing doubt CI's enormous potential value to the future of computing. Unfortunately, for lack of a rigorous, formal theory of Collective Intelligence, most attempts to analyze CI systems have been disappointing, at best. In Computational Collective Intelligence, Professor Tadeusz Szuba does much to rectify that situation by developing, for the first time, both a formal definition of CI and practical guidelines for its assessment and applications. Working from the ground up, Dr. Szuba begins with a stimulating and insightful discussion of the types of intelligence-including individual, artificial, and collective-into which he brings ideas from AI, information theory, and distributed computing, as well as psychology, sociology, animal behavior, cognitive science, and other relevant disciplines. He tackles the problem of computational models for simulating and measuring CI. He explores all theoretically feasible models of CI computations and presents a groundbreaking, nondeterministic approach using the Random PROLOG Processor (RPP) as a CI modeling and evaluation tool. He then introduces the Collective Intelligence Quotient (IQS) and develops clear-cut guidelines for measuring it. In the final chapters, he lays the foundation for a dynamic new discipline, Collective Intelligence Engineering (CIE), and considers its potential applications as an organizational restructuring tool. |
From inside the book
Results 1-3 of 71
Page xii
... observed and been surprised at how life or research problems become almost immediately eased , if not solved , by a cooperating group , for example , during free , relaxed discussion such as at a party or a conference . Collective ...
... observed and been surprised at how life or research problems become almost immediately eased , if not solved , by a cooperating group , for example , during free , relaxed discussion such as at a party or a conference . Collective ...
Page 106
... observed that high status members of the group have more freedom in departure from group norms and are less affected by the pressure of conformity . • Size The size of the group seriously affects its behavior . Most researchers define a ...
... observed that high status members of the group have more freedom in departure from group norms and are less affected by the pressure of conformity . • Size The size of the group seriously affects its behavior . Most researchers define a ...
Page 170
... observe interactions made up of loosely coupled individual computational processes . Individual computational processes ( for ... observed over a rich spectrum of beings that are all · · very different . Thus , we have to 170 TOWARD THE ...
... observe interactions made up of loosely coupled individual computational processes . Individual computational processes ( for ... observed over a rich spectrum of beings that are all · · very different . Thus , we have to 170 TOWARD THE ...
Contents
IntelligenceOur Present State of Understanding | 11 |
Problems when a Social Structure Grows | 131 |
66 | 157 |
Copyright | |
7 other sections not shown
Common terms and phrases
abilities Abstract Machine Adleman's agents algorithm analog computers analyze ants approach architecture artificial artificial intelligence assumed backtrack bacteria basic behavior Brownian clause collective intelligence communication complexity computational process computational space computer graphics concept configuration cooperation create defined definition of collective denoted deterministic Deterministic Turing Machine difficulties digital computers discussed displacements DNA computer dynamical elements emerge environment evaluation example execution Expert Systems fact factors formal function given in Figure goals Horn clauses human implemented individual intelligence inference process information molecules input interaction language let us look logical mathematical membrane model of computations multiset NDTM necessary nondeterministic Nondeterministic Turing Machine operations organization parallel point of view possible predicate present problem PROLOG program quasi-chaotic Random PROLOG Processor rendezvous result retract rules simulation social structure solution solve specific tasks Turing Machine unification variables Warren Abstract Machine
References to this book
Intelligent Cities and Globalisation of Innovation Networks Nicos Komninos No preview available - 2008 |