Software Agent 인지 구조

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Software Agent 인인 인인 John R. Anderson, "Human Symbol Manipulation Within an Integrated Cognitive Architecture," Cognitive Science, vol. 29, no. 3, pp. 313–341, 2005 4 주주 : 주 1 주주 주주주주 / 주주주 : 주주주

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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 - PowerPoint PPT Presentation

Transcript of Software Agent 인지 구조

Page 1: Software Agent  인지 구조

Software Agent 인지 구조

John R. Anderson, "Human Symbol Manipulation Within an Integrated Cognitive Architecture,"

Cognitive Science, vol. 29, no. 3, pp. 313–341, 2005

4 주차 : 제 1 발제 인지구조 / 발제자 : 최봉환

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Outline

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

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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

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ACT-R Theory

• ACT-R– Adaptive Control of Thought–Rational

= cognitive architecture

• Theory– for "how human cognition works"

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ACT-R Architecture

• RoleInput = Problem repre-

sentation(3x - 5 = 7)

Mental representa-tion

(3x = 12)

Communication,Procedural Control

Goal : Strategy decision(unwind stratage)

Retrieve Critical In-formation(7+5=12)

Output(x=4)

massive parallelism & central bottle neck

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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

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The ACT–R model

• General instruction–

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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

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Regions of interest

Caudate procedural

prefrontal retrieval Anterior cingulate

goal

Paretal problem state or

imaginalmotormanual

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Measuring activity

• Measuring activity– BOLD : blood-oxygen-level-dependent

• measure neural activity directly have been attempted

– profile of activity in modules

• t = time, s = scales the time, a = determines the shape of BOLD response,m = govern magnitude

• f(x) = engage function

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Characterizing the differences among the brain regions

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Assessing goodness of fit

• Measure the degree of mismatch against the noise in the data

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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=4 라면 내부구조의 의미는 ?

• 성능은 ?– 간단한 문제라서 잘 풀리는 것이 아닌지 ?– 수학적인 문제 혹은 논리적인 문제에만 적용 가능한 건 아닌지

• 모호함에 대한 해결책은 ?– ACT-R 은 Deliberative Agent 인듯한데 모호한 정의에 대한 묘사는

어떻게 ?– Goal based Agent 로 구성되어 있는데 목적지는 어떻게 찾을 것인가 ?