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Software Agent 인지 구조

Software Agent 인지 구조. 4 주차 : 제 1 발제 인지구조 / 발제자 : 최봉환. John R. Anderson, "Human Symbol Manipulation Within an Integrated Cognitive Architecture," Cognitive Science, vol. 29, no. 3, pp. 313–341, 2005. Outline. Introduction ACT-R Use of brain imaging

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Software Agent 인지 구조

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  1. Software Agent 인지 구조 4주차 : 제 1 발제 인지구조 / 발제자 : 최봉환 John R. Anderson, "Human Symbol Manipulation Within an Integrated Cognitive Architecture," Cognitive Science, vol. 29, no. 3, pp. 313–341, 2005

  2. Outline • Introduction • ACT-R • Use of brain imaging • The capacity for re-representation: A uniquely human trait?

  3. Introduction • Overview of ACT-R theory • illustrative application of it to algebra equation solving • Algebra equation solving • uniquely human cognitive activity • "what is unique about human cognitive?" • Comparing human brain with ACT-R • preliminary mapping ACT-R component to brain  functional fMRI

  4. ACT-R Theory • ACT-R • Adaptive Control of Thought–Rational = cognitive architecture • Theory • for "how human cognition works"

  5. ACT-R Architecture • Role Input = Problem representation (3x - 5 = 7) Output (x=4) massive parallelism & central bottle neck Mental representation (3x = 12) Retrieve Critical Information (7+5=12) Communication, Procedural Control Goal : Strategy decision (unwind stratage)

  6. Algebra equation manipulation • Why algebra equation solving problem • substantial complexity • tractably characterized and studied • unlike many human accomplishments (cf : Natural language) • Problem • solved by unwind strategy 

  7. The ACT–R model • General instruction  

  8. The ACT–R model : speedup • Speedup • Compilation • collapse multiple steps into single step • Reduction of retrieval times • subsymbolic learning • instruction strongly encoded during day0 • arithmetic fact repeated  major learning happening at the symbolic level • production rules

  9. motor manual Paretal  problem state or imaginal Regions of interest Anterior cingulate  goal Caudate  procedural prefrontal  retrieval

  10. Measuring activity • Measuring activity • BOLD : blood-oxygen-level-dependent • measure neural activity directly have been attempted • profileof activity in modules • t = time, s = scales the time, a = determines the shape of BOLD response,m = govern magnitude • f(x) = engage function 

  11. Characterizing the differences among the brain regions

  12. Assessing goodness of fit • Measure the degree of mismatch against the noise in the data • 

  13. 토의 제안 • 인간과 동일한 구조를 모사하는 것의 의미는? • 인간과 동일할 필요가 있는가? • 인간에게 원하는 것과 컴퓨터에게 원하는 것이 다를 것 같은데.. • 인간과 동일한 것을 증명할 필요는 있는가? • 1+3 = 4 = 2+2=4라면 내부구조의 의미는? • 성능은? • 간단한 문제라서 잘 풀리는 것이 아닌지? • 수학적인 문제 혹은 논리적인 문제에만 적용 가능한 건 아닌지 • 모호함에 대한 해결책은? • ACT-R은 Deliberative Agent인듯한데 모호한 정의에 대한 묘사는 어떻게? • Goal based Agent로 구성되어 있는데 목적지는 어떻게 찾을 것인가?

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