Problem-Solving Methods: Understanding, Description, Development, and ReuseSpringer Science & Business Media, 27 սեպ, 2000 թ. - 153 էջ Researchers in Artificial Intelligence have traditionally been classified into two categories: the “neaties” and the “scruffies”. According to the scruffies, the neaties concentrate on building elegant formal frameworks, whose properties are beautifully expressed by means of definitions, lemmas, and theorems, but which are of little or no use when tackling real-world problems. The scruffies are described (by the neaties) as those researchers who build superficially impressive systems that may perform extremely well on one particular case study, but whose properties and underlying theories are hidden in their implementation, if they exist at all. As a life-long, non-card-carrying scruffy, I was naturally a bit suspicious when I first started collaborating with Dieter Fensel, whose work bears all the formal hallmarks of a true neaty. Even more alarming, his primary research goal was to provide sound, formal foundations to the area of knowledge-based systems, a traditional stronghold of the scruffies - one of whom had famously declared it “an art”, thus attempting to place it outside the range of the neaties (and to a large extent succeeding in doing so). |
Բովանդակություն
What Are ProblemSolving Methods | 6 |
How to Describe ProblemSolving Methods | 41 |
How to Develop and Reuse ProbelemSolving Methods | 92 |
Conclusions and Future Work | 129 |
Այլ խմբագրություններ - View all
Problem-Solving Methods: Understanding, Description, Development, and Reuse Dieter Fensel Դիտել հնարավոր չէ - 2000 |
Common terms and phrases
abduction Abstract State Machines achieved adapter algorithmic applied approaches architecture axioms characterize CommonKADS complete constraints context correct Davis & Hamscher define derived describe design model design problem domain knowledge domain model dynamic logic efficiency elements existence explain(H explanation fault Fensel first-order logic formal framework global optimum goal Harmelen heuristic hill-climbing hypotheses implementation inference action input instantiation interactions introducing assumptions KARL Kleer & Williams knowledge engineering knowledge roles knowledge-based system local search minimal MLCM MLPM Modal Logic model-based diagnosis Motta Nodes object ontological operational specification optimal output parameters parametric design parsimonious predicate problem definition problem type problem-solving method problem-solving process proof obligations propose & revise reasoning process refinement requirements on domain reusable reuse search method search process semantics set-minimizer situation calculus solution solver solving method step structure subtasks successor task definition task layer variables variants verification