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level: Level 1

Questions and Answers List

level questions: Level 1

QuestionAnswer
What are the two components of Automated Reasoning?Knowledge Base (KB): what we know about the world (a domain of interest, e.g. number theory, medicine) Inference Engine: how we think; used to answer queries and derive implicit knowledge about the world (modeled domain)
What was the Initial proposal in early days of AI?Deductive Logic Top-down approach to logical thinking where conclusions are derived from general principles. All dogs have ears; golden retrievers are dogs, therefore they have ears.
What is the weakness of Deductive Logic?Deductive Logic fails in a domain like medical diagnosis (and others such as: law, business, automobile repair, gardening, dating) due to: Laziness Theoretical ignorance Practical ignorance
What are the Limits of Deduction Logic?Deductive Logic is monotonic Once we deduce something from a KB, we can never invalidate the deduction by acquiring more knowledge
What is the Qualification Problem?To deduce a conclusion without relying on assumptions by meeting all necessary preconditions. e.g. must have two wings, must not be afraid of flying, must have already learned how to fly, etc.
What are some solutions to monotonic logics?Solution 1: Non-Monotonic Logics Solution 2: Degree of Belief
What is Non-Monotonic Logics?Equipping logic with the ability to jump into certain conclusions Requires: − Mechanisms for managing assumptions − Criteria for deciding on which assumptions to assert and retract, and when Consistency-based approach: • Assert as many assumptions as possible, as long as they do not lead to a logical inconsistency
What is the problem with Non-Monotonic Logics?They have contradicting extensions/conclusions!
How do you resolve the conflict for Non-Monotonic Logics?Belief Revision in non-monotonic logics Use the notion of a Degree of Belief and probabilistic reasoning
What is a Degree of Belief?Instead of declaring facts (Deductive Logic) or assumptions (Non-monotonic Logics), assign a degree of belief to propositions
Degrees of belief assigned are..Interpreted as probabilites Manipulated by the laws of probability
De Finetti‘s Theorem (1931):Impossible to act rationally under uncertainty using degrees of belief that violate axioms of probability.
What does Probability mean? What are the two approaches?Objectivist (frequentist) approach Subjectivist (Bayesian) approach
What is a Objectivitist (frequentist) approach?Probabilities as − inherent objective properties of objects − real aspects of the universe − view of probability based in the Law of Large Numbers (prob. of event corresponds to relative frequency of over infinite number of trials) • Source of probability numbers: (only) experiments
What is an example of a frequentist approach?Frequentist probability „a patient has cavity“ • Interpretation: Among 100 examined patients, we should detect approximately 30 times a cavity
What is a Subjectivist (Bayesian) approach:probability as − quantifies subjective belief in the occurrence of an event − reflects state of knowledge of an individual (person, agent) • Allows any self-consistent ascription of prior probabilities to propositions • Insists on proper Bayesian updating as new evidence arrives
What is an example of a Subjectivist (Bayesian) approach?Bayesian probability „a patient has cavity“ • Interpretation: With confidence 30% I believe that a particular patient belongs to those people who have a cavity
Example of Degree of BeliefWe see the bird on the right (E0) • „this bird is normal“ since no evidence that • something is wrong with this bird → derive „this bird can fly“ Then we see this bird from a new viewing angle (E1) • Update based on E1 • Update belief of „this bird is normal“ → Update belief of „this bird fly“
Degree of Belief vs. AssumptionsAssumptions - Either true or false Decisions tend to follow naturally from the assumptions made Pro: less information is needed Con: Derived facts might turn out to be false later Degree of Belief: Continuous in interval , gives more information than assumptions Decision-making: Decision Theory = Prob. Theory + Utility Theory Con: more information is needed Pro: degrees of belief do not imply any particular truth of the underlying propositions, pitfalls can be avoided