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CS530 Intro Concepts

AB
Strong AIclaim that computers can be made to actually think, just like humans
Weak AIclaim that computers are important tools in the modeling and simulation of human activity
Declarative knowledgeknowledge that is made explicit, knowing that something is the case
Procedural knowledgeknowing how to do something, reacting to a situation on basis of acquired skill or experience
Semantic knowledgelong-term knowledge that reflects the meaning of representations
Episodic knowledgebiographical or experiential form of knowledge
Factual or syntactic knowledgebasic knowledge about the domain of interest
Semantic knowledgeKnowledge of concepts that underlie problems
Schematic knowledgeKnowlege of problem types
Strategic knowledgeKnowledge of strategies for generating, monitoring, and modifying plans
Heuristicexperience-based technique that helps in problem solving, learning, and discovery
Anchoringtendency to rely to heavily or "anchor" on one trait or piece of information when making decisions
Availabilitytendency to base predictions of the frequency of an event or the proportion within a population on how easily an example can be brought to mind
Represenativenesstendency to assume commonality between objects of similar appearance, or between an object and a group it seems to fit into.
Naïve diversificationtendency to seek more variety when making simultaneous choices than when making sequential choices
Escalation of commitmenttendency to justify increased investment in a decision based on the cumulative prior investment, despite new evidence suggesting that the decision was probably wrong
Efforttendency to assign value to an object based on the amount of perceived effort that went into producing the object
Familiartytendency to judge a current behavior to be correct based on how similar it is to a past behavior and its outcomes
Fluencytendency to assign a higher value to one object over another if it is processed more fluently, faster, or more smoothly
Peak-endtendency to judge past experiences in terms of how they were at their peak and how they ended
Recognitiontendency to give a higher value with respect to a criterion to an object if that object is recognized and the other is not
Scarcitytendency to value an object based on how easily it may be lost, especially to competitors
Simulationtendency to substitute "normal" antecedent events for exceptional ones when attempting to understand how an event occurred
Take-the-besttendency to order multiple criteria in terms of their perceived relevance to one or another category and make a decision based on the first criterion which discriminates between the alternatives


Sam Gillis

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