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020 _a9783540877028
_9978-3-540-87702-8
024 7 _a10.1007/978-3-540-87702-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 4 _aThe Challenge of Anticipation
_h[electronic resource] :
_bA Unifying Framework for the Analysis and Design of Artificial Cognitive Systems /
_cedited by Giovanni Pezzulo, Martin V. Butz, Cristiano Castelfranchi, Rino Falcone.
250 _a1st ed. 2008.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2008.
300 _aXVI, 288 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v5225
505 0 _aTheory -- Introduction: Anticipation in Natural and Artificial Cognition -- The Anticipatory Approach: Definitions and Taxonomies -- Benefits of Anticipations in Cognitive Agents -- Models, Architectures, and Applications -- Anticipation in Attention -- Anticipatory, Goal-Directed Behavior -- Anticipation and Believability -- Anticipation and Emotions for Goal Directed Agents -- A Reinforcement-Learning Model of Top-Down Attention Based on a Potential-Action Map -- Anticipation by Analogy -- Anticipation in Coordination -- Endowing Artificial Systems with Anticipatory Capabilities: Success Cases.
520 _aThis book proposes a unifying approach for the analysis and design of artificial cognitive systems: The Anticipatory Approach. In 11 coherent chapters, the authors of this State-of-the-Art Survey propose a foundational view of the importance of dealing with the future, of gaining some autonomy from current environmental data, and of endogenously generating sensorimotor and abstract representations. A meaningful taxonomy for anticipatory cognitive mechanisms is put forward, which distinguishes between the types of predictions and the different influences of these predictions on actual behavior and learning. Thus a new unifying perspective on cognitive systems is given. The Anticipatory Approach described in this book will not only aid in the analysis of cognitive systems, but will also serve as an inspiration and guideline for the progressively more advanced and competent design of large, but modular, artificial cognitive systems.
650 0 _aArtificial intelligence.
650 0 _aComputer programming.
650 0 _aComputer simulation.
650 0 _aComputer science.
650 0 _aComputer science
_xMathematics.
650 0 _aMathematical statistics.
650 0 _aUser interfaces (Computer systems).
650 0 _aHuman-computer interaction.
650 1 4 _aArtificial Intelligence.
650 2 4 _aProgramming Techniques.
650 2 4 _aComputer Modelling.
650 2 4 _aModels of Computation.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aUser Interfaces and Human Computer Interaction.
700 1 _aPezzulo, Giovanni.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aButz, Martin V.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aCastelfranchi, Cristiano.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aFalcone, Rino.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540877011
776 0 8 _iPrinted edition:
_z9783540877240
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v5225
856 4 0 _uhttps://doi.org/10.1007/978-3-540-87702-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cSPRINGER
999 _c183693
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