Ping Pong in Church: Productive use of concepts in human probabilistic inference
نویسندگان
چکیده
(mh-query 1000 100 ;Monte Carlo Inference ;CONCEPTS (define personstrength (mem (lambda (person) (gaussian 10 3)))) (define lazy (mem (lambda (person game) (flip 0.1)))) (define (teamstrength team game) (sum (map (lambda (person) (if (lazy person game) (/ (personstrength person) 2) (personstrength person))) team))) (define (winner team1 team2 game) (if (< (teamstrength team1 game) (teamstrength team2 game)) 'team2 'team1)) ;QUERY (personstrength 'A) ;EVIDENCE (and (= 'team1 (winner '(TG) '(NG) 1)) (= 'team1 (winner '(NG) '(AS) 2)) (= 'team1 (winner '(NG) '(BL) 3)) (lazy '(NG) 1) ;additional evidence, used in Experiment 2 ) ) How do people make inferences from complex patterns of evidence across diverse situations? What does a computational model need in order to capture the abstract knowledge people use for everyday reasoning?
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