%0 Book %1 lakemeyer94 %A Lakemeyer, G. %A Nebel, B. %B Foundations of Knowledge Representation %C New York %D 1994 %I Springer-Verlag %K representation knowledge %T Foundations of Knowledge Representation and Reasoning: A guide to this volume %U http://www.cs.toronto.edu/~nernst/papers/foundations-of-knowledge-representation.pdf

@book{lakemeyer94, added-at = {2006-03-24T16:34:33.000+0100}, address = {New York}, author = {Lakemeyer, G. and Nebel, B.}, biburl = {https://www.bibsonomy.org/bibtex/28e5e419b6c15c348e7c1962e412e248b/neilernst}, citeulike-article-id = {121757}, comment = {KR hypothesis: knowledge is represented declaratively in some language, 2) representations play a causal role in engendering the behaviour of the system. - thus, questions are: what is the right language; what inferences can we draw from a KB; how do we incorporate new knowledge? - should it be a logic language? See Hayes 1977 - defeasible = re-deduce the assumption based on new circumstances . Modelled classically by non-monotonic logic and probability. Probability is that what we as humans actually use? We should have amethod whcich allows us to reason without being certain all the time. - expressiveness vs. efficiency tradeoff - in logic we make assumptions about how far I can jump, that is, the statement made is either true or false. My system assigns a probability of truth ot the proposition or predicate, indicating how highly we believe in that statement.}, description = {sdasda}, interhash = {15883a048affab4ce93eb2bb0eb93d47}, intrahash = {8e5e419b6c15c348e7c1962e412e248b}, keywords = {representation knowledge}, priority = {0}, publisher = {Springer-Verlag}, series = {Foundations of Knowledge Representation}, timestamp = {2006-03-24T16:34:33.000+0100}, title = {Foundations of {K}nowledge {R}epresentation and {R}easoning: {A} guide to this volume}, url = {http://www.cs.toronto.edu/~nernst/papers/foundations-of-knowledge-representation.pdf}, year = 1994 }