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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

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

outline
Outline
  • Introduction
  • ACT-R
  • Use of brain imaging
  • The capacity for re-representation: A uniquely human trait?
introduction
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
act r theory
ACT-R Theory
  • ACT-R
    • Adaptive Control of Thought–Rational

= cognitive architecture

  • Theory
    • for "how human cognition works"
act r architecture
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)

algebra equation manipulation
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

the act r model
The ACT–R model
  • General instruction

the act r model speedup
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
regions of interest

motor

manual

Paretal

 problem state or imaginal

Regions of interest

Anterior cingulate

 goal

Caudate  procedural

prefrontal

 retrieval

measuring activity
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

assessing goodness of fit
Assessing goodness of fit
  • Measure the degree of mismatch against the noise in the data
slide13
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?