Symbolic Artificial intelligence

1 symbolic

1.1 cognitive simulation
1.2 logic-based
1.3 anti-logic or scruffy
1.4 knowledge-based

symbolic

when access digital computers became possible in middle 1950s, ai research began explore possibility human intelligence reduced symbol manipulation. research centered in 3 institutions: carnegie mellon university, stanford , mit, , each 1 developed own style of research. john haugeland named these approaches ai old fashioned ai or gofai . during 1960s, symbolic approaches had achieved great success @ simulating high-level thinking in small demonstration programs. approaches based on cybernetics or neural networks abandoned or pushed background. researchers in 1960s , 1970s convinced symbolic approaches succeed in creating machine artificial general intelligence , considered goal of field.

cognitive simulation

economist herbert simon , allen newell studied human problem-solving skills , attempted formalize them, , work laid foundations of field of artificial intelligence, cognitive science, operations research , management science. research team used results of psychological experiments develop programs simulated techniques people used solve problems. tradition, centered @ carnegie mellon university culminate in development of soar architecture in middle 1980s.

logic-based

unlike newell , simon, john mccarthy felt machines did not need simulate human thought, should instead try find essence of abstract reasoning , problem solving, regardless of whether people used same algorithms. laboratory @ stanford (sail) focused on using formal logic solve wide variety of problems, including knowledge representation, planning , learning. logic focus of work @ university of edinburgh , elsewhere in europe led development of programming language prolog , science of logic programming.

anti-logic or scruffy

researchers @ mit (such marvin minsky , seymour papert) found solving difficult problems in vision , natural language processing required ad-hoc solutions – argued there no simple , general principle (like logic) capture aspects of intelligent behavior. roger schank described anti-logic approaches scruffy (as opposed neat paradigms @ cmu , stanford). commonsense knowledge bases (such doug lenat s cyc) example of scruffy ai, since must built hand, 1 complicated concept @ time.

knowledge-based

when computers large memories became available around 1970, researchers 3 traditions began build knowledge ai applications. knowledge revolution led development , deployment of expert systems (introduced edward feigenbaum), first successful form of ai software. knowledge revolution driven realization enormous amounts of knowledge required many simple ai applications.