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Knowledge Representation • • • • • • • why bother ? what is it ? what do we represent ? how is it represented ? Kn Repn strategies inferencing example tasks why bother ? intelligence & knowledge • • • • • • reacting to sensory infm using tools communication learning human knowledge organisation the messed up species psychological clues • interlinking • voice – person – mood • sound/smell – memory – reminiscence • vis-ob – properties – ownership • cognitive limitations • • • • levels of infm & retrieval attention short-term memory field dependency physiological clues • perceptive pre-processing • brain areas & malfunctions • • • • memory freezing the elephant's toenail unknown ownership / name the speechless monk (Aitchison P.39) who cares? • feathers & flight • random sorts • chaos, complexity & emergent behaviour why bother ? a language example • The old man the boats. • I saw the racing pigeons flying to Paris. • I saw the Eiffel Tower flying to Paris. • The boy kicked the ball under the tree. • The boy kicked the wall under the tree. • Put the apple in the basket on the shelf what is it ? • declarative forms data facts • procedural forms processing retrieval / linking / lumping • inference what do we represent ? • • • • objects (& their relationships) events (& sequences) performance (& cause & effect) meta kn – extent, priority, strategy, reliability, importance n K • • • • • • n Rep strategies facts & rules logic semantic nets frames scripts conceptual dependency a simple semantic net animal if has( wings ) flying elsif has( legs ) walking else crawling moves_by ako brown color bird ako sparrow has wings ako budgie ako freddie color yellow general capabilities • • • • inheritance defaults demons perspectives inferencing • • • • rule application & deduction generalisation detecting similarity measuring differences tasks • • • • • • automatic rule generation IQ tests learning (near miss) planning agent interaction (conflict & co-operation) human dialog management