Mind design
Approaches to knowledge representation: an introduction
Approaches to knowledge representation: an introduction
Neurocomputing: foundations of research
Neurocomputing: foundations of research
Unified theories of cognition
John von Neumann and the origins of modern computing
John von Neumann and the origins of modern computing
Models of my life
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
What computers still can't do: a critique of artificial reason
What computers still can't do: a critique of artificial reason
Neurocomputing (vol. 2): directions for research
Neurocomputing (vol. 2): directions for research
Artificial intelligence in perspective
Artificial intelligence in perspective
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Understanding, building and using ontologies
International Journal of Human-Computer Studies
Knowledge engineering and management: the CommonKADS methodology
Knowledge engineering and management: the CommonKADS methodology
Computer science as empirical inquiry: symbols and search
Communications of the ACM
Introduction to AI Robotics
Conceptual Coordination: How the Mind Orders Experience in Time
Conceptual Coordination: How the Mind Orders Experience in Time
An Behavior-based Robotics
Situated Cognition: On Human Knowledge and Computer Representations
Situated Cognition: On Human Knowledge and Computer Representations
Encyclopedia of Artificial Intelligence
Encyclopedia of Artificial Intelligence
Handbook of AI
Membrane Computing: An Introduction
Membrane Computing: An Introduction
Autonomous robotic systems
Experiences in reusing knowledge sources using Protégé and PROMPT
International Journal of Human-Computer Studies - Protégé: community is everything
On how the computational paradigm can help us to model and interpret the neural function
Natural Computing: an international journal
Quantum Computation and Quantum Information: 10th Anniversary Edition
Quantum Computation and Quantum Information: 10th Anniversary Edition
On the physical formal and semantic frontiers between human knowing and machine knowing
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
On some of the neural mechanisms underlying adaptive behavior
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Editorial: The internal observer and the semantic gap
Neurocomputing
Revisiting Algorithmic Lateral Inhibition and Accumulative Computation
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
General theory of exobehaviours: a new proposal to unify behaviors
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Granular representation and granular computing with fuzzy sets
Fuzzy Sets and Systems
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
Decision Support Systems
From Numeric to Granular Description and Interpretation of Information Granules
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
| Hi-index | 0.01 |
Artificial intelligence (AI) was born connectionist when in 1943 Warren S. McCulloch and Walter Pitts introduced the first sequential logic model of neuron. The 1950s sees the passage from numerical to symbolic computation with the christening of AI in 1956. In 1986, there is a rebirth of connectionism at the same time that an emphasis in knowledge modeling and inference, both symbolic and connectionist. We thus reach the present state in which different paradigms coexist (symbolic, connectionist, situated and hybrid). In this work, we will attempt (1) to approach the concept of AI both as a science of the natural and as knowledge engineering (KE); (2) summarize some of the conceptual, formal and methodological approaches to the development of AI during the last 50 years, (3) mention some of the constitutive differences between human knowing and machine knowing and (4) propose some suggestions that we believe must be adopted to progress in developing AI.