Artificial Intelligence
Principles of artificial intelligence
Principles of artificial intelligence
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
Task-structure analysis for knowledge modeling
Communications of the ACM - Special issue on analysis and modeling in software development
KADS: a modelling approach to knowledge engineering
Knowledge Acquisition - Special issue on the KADS approach to knowledge engineering
Knowledge-based systems analysis and design
Knowledge-based systems analysis and design
Structure-preserving specification languages for knowledge-based systems
International Journal of Human-Computer Studies - Special issue: verification and validation
Applying a library of problem-solving methods on a real-life task
International Journal of Human-Computer Studies
International Journal of Human-Computer Studies
A Generic Formulation of Neural Nets as a Model of Parallel and Self-Programming Computation
IWANN '97 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Biological and Artificial Computation: From Neuroscience to Technology
Knowledge Technology: Moving into the Next Millenium
IEA/AIE '98 Proceedings of the 11th international conference on Industrial and engineering applications of artificial intelligence and expert systems: methodology and tools in knowledge-based systems
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Autonomous robotic systems
Key aspects of the diagen conceptual model for medical diagnosis
IWINAC'05 Proceedings of the First international conference on Mechanisms, Symbols, and Models Underlying Cognition: interplay between natural and artificial computation - Volume Part I
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The basic conjecture in this paper is that besides thebasic libraries of tasks and problem-solving methods (PSMs), it isnecessary to develop two complementary twin libraries. One ofthem consists of knowledge-acquisition schemas, as they arerequired by PSMs, and the other one contains those PSMs‘ reductionmethods, from the knowledge level to the symbol level. In order tosupport this conjecture, we first describe the reduction methodfundamentals based on hierarchical graphs representing the underlyingcomputational model. Then we shall comment on the development ofthe SCHEMA interface; by using this interface, we can directly obtain theprogram code, provided the task knowledge, PSM, and application domainare edited following the knowledge acquisition schemas by means ofstructured natural language sentences. This kind of editingunmistakably and in reversibly establishes the relationships with theunderlying model. Since the reduction method links the underlyingmodel with the program code, the reduction process is completed.Conversely, we can retrieve the underlying model graph and theknowledge-level model from the program code because of thereversibility of the reduction method. In order to make clear thereduction method and SCHEMA interface functioning from the userviewpoint, we shall apply them to a classification and diagnosegeneric task (Hierarchical Classification), which will be decomposedby using the “Establish and Define” PSM, and another task to carryout a plan, which will be decomposed by using the “act-check-decide”PSM. We shall finish this paper with a reflection on the knowledge-level modelling and the necessity of an increase of the availablereduction methods and knowledge acquisition schemas which areincluded in our SCHEMA interface.