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A Different Kind of KR COMP34512 Sebastian Brandt brandt@cs.manchester.ac.uk Friday, 2 May 2014 Varieties of KR Weve been looking at a fairly narrow set Mostly (first order) logic based Ontology languages Primarily

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A Different Kind of KR COMP34512

Sebastian Brandt brandt@cs.manchester.ac.uk

Friday, 2 May 2014

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Varieties of KR

  • We’ve been looking at a fairly narrow set

– Mostly (first order) logic based – “Ontology” languages

  • Primarily “conceptual” knowledge

– Good for terminologies other data management

  • Wide range of possibilities

– Other than first order logics

  • Modal, probabilistic, temporal, spatial, nonmonotonic, etc.

– Code! – Semantic nets (SKOS), production rules, Bayesian networks – Etc. etc. etc.

  • Often determined

– By needs of the domain

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Friday, 2 May 2014

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Representational Domain

  • Recall cognitive adequacy

– Weak ≈ usability – Strong ≈ how we think

  • A case where SCA is (perhaps) required

– When we want to represent human minds!

  • Why?

– To test (or enact) psychological theories – Cognitive Tutors – Usability testing – Human-like intelligences?!??!

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Cognitive Architectures

  • Idea

– A general, typically comprehensive, model of cognition

  • Or the foundations of cognition
  • Or basic structure of cognition
  • A unifying model of cognition
  • Tools for building cognitive/intelligent agents

– At least, psychologically plausible

  • Ideally, psychologically accurate!
  • Many proposed and used

– Symbolic

  • ACT-R, SOAR, EPIC, etc.
  • Thought ≈ manipulation of symbols
  • “Language of thought”

– Connectionist

  • E.g., Neural networks, computational neuroscience (SpiNNaker)
  • Thought ≈ patterns of activation in simple connected units

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CogArchs as KR formalisms

  • Symbolic Cognitive Architectures

– Often have a “language”

  • Symbols and symbol manipulation after all

– Syntax

  • Usually clear

– Semantics

  • Not so clear

– Reasoning procedure

  • Also not always clear
  • They face similar design tradeoffs

– Expressivity, usability, computability

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ACT-R

  • “Adaptive Control of Thought—Rational”

– Several specialized modules

  • Visual module, long term memory, manual module, goal module, etc.

– Central production system coordinates

  • “Production” rules push and pull information between modules

– Mapping onto neural substrates

  • Other biological constraints
  • E.g., 50 ms “cognition cycle”’

– Historical bias

  • toward high level cognition

– Address many aspects

  • Acquisition, learning
  • “Compilation”
  • Decay
  • Error

6 http://act-r.psy.cmu.edu/papers/526/FSQUERY.pdf

Friday, 2 May 2014

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Schematic Overview

7 http://act-r.psy.cmu.edu/about/

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ACT-R Kinds of Knowledge

  • Declarative

– Articulable knowledge; propositional – Represented as “chunks”

  • I.e., typed sets of key-value pairs (i.e., “slots”)
  • A weak sort of object

– Stored in the declarative memory

  • “Viewed” through the “retrieval buffer”
  • Limited access! Must “recall” knowledge!
  • Procedural

– Actionable “ways of doing things” – Production rules – Stored in procedural memory – NOT directly retrievable

  • Triggered by goals and retrieved knowledge (chunks “in mind”)
  • Manipulates goals, retrieves knowledge, alters knowledge

– Both declarative and procedural!

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Counting example

  • How to capture counting from 2 to 4
  • We need to have declarative knowledge

– That after 2, comes 3, and after 3, comes 4

  • We need to have procedural knowledge

– That to “count from X to Y”, we

  • Start at X
  • Go to the next number, N
  • If N = Y, we made it!
  • Otherwise, repeat with X = N

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Counting example: Declarative

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(add-dm (b ISA count-order first 1 second 2) (c ISA count-order first 2 second 3) (d ISA count-order first 3 second 4) (e ISA count-order first 4 second 5) (f ISA count-order first 5 second 6) (first-goal ISA count-from start 2 end 4))

(b ISA count-order first 1 second 2)

Is this psychologically realistic?

(chunk-type count-order first second) (chunk-type count-from start end count)

From http://act-r.psy.cmu.edu/tutorials/

Friday, 2 May 2014

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Counting example: Procedural

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(P increment =goal> ISA count-from count =num1

  • end =num1

=retrieval> ISA count-order first =num1 second =num2 ==> =goal> count =num2 +retrieval> ISA count-order first =num2 !output! (=num1) ) If The goal is to count from

  • ur current count

but we’re not done and we have “in mind” in the normal order given where we are what comes next Then Change the goal to count from the next nr and recall What comes next after our current count (Count out loud!)

Is this what we do when counting?

From http://act-r.psy.cmu.edu/tutorials/

Friday, 2 May 2014

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Counting example: Setup

  • We focused on the counting

– Not on what would cause us to count!

  • “Would you please count aloud from 2 to 4!”

– But we still need to initiate action

  • So we gin up some bits

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(P stop =goal> ISA count-from count =num end =num ==>

  • goal>

!output! (=num) ) (p start =goal> ISA count-from start =num1 count nil ==> =goal> count =num1 +retrieval> ISA count-order first =num1 ) (goal-focus first-goal)

... (first-goal ISA count-from start 2 end 4))

From http://act-r.psy.cmu.edu/tutorials/

Friday, 2 May 2014

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Counting!

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(run 1) 0.000 GOAL SET-BUFFER-CHUNK GOAL FIRST-GOAL REQUESTED NIL 0.000 PROCEDURAL CONFLICT-RESOLUTION 0.000 PROCEDURAL PRODUCTION-SELECTED START 0.000 PROCEDURAL BUFFER-READ-ACTION GOAL 0.050 PROCEDURAL PRODUCTION-FIRED START 0.050 PROCEDURAL MOD-BUFFER-CHUNK GOAL 0.050 PROCEDURAL MODULE-REQUEST RETRIEVAL 0.050 PROCEDURAL CLEAR-BUFFER RETRIEVAL 0.050 DECLARATIVE START-RETRIEVAL 0.050 PROCEDURAL CONFLICT-RESOLUTION 0.100 DECLARATIVE RETRIEVED-CHUNK C 0.100 DECLARATIVE SET-BUFFER-CHUNK RETRIEVAL C 0.100 PROCEDURAL CONFLICT-RESOLUTION 0.100 PROCEDURAL PRODUCTION-SELECTED INCREMENT 0.100 PROCEDURAL BUFFER-READ-ACTION GOAL 0.100 PROCEDURAL BUFFER-READ-ACTION RETRIEVAL 0.150 PROCEDURAL PRODUCTION-FIRED INCREMENT 2 0.150 PROCEDURAL MOD-BUFFER-CHUNK GOAL 0.150 PROCEDURAL MODULE-REQUEST RETRIEVAL 0.150 PROCEDURAL CLEAR-BUFFER RETRIEVAL 0.150 DECLARATIVE START-RETRIEVAL 0.150 PROCEDURAL CONFLICT-RESOLUTION 0.200 DECLARATIVE RETRIEVED-CHUNK D 0.200 DECLARATIVE SET-BUFFER-CHUNK RETRIEVAL D 0.200 PROCEDURAL CONFLICT-RESOLUTION 0.200 PROCEDURAL PRODUCTION-SELECTED INCREMENT 0.200 PROCEDURAL BUFFER-READ-ACTION GOAL 0.200 PROCEDURAL BUFFER-READ-ACTION RETRIEVAL 0.250 PROCEDURAL PRODUCTION-FIRED INCREMENT 3 0.250 PROCEDURAL MOD-BUFFER-CHUNK GOAL 0.250 PROCEDURAL MODULE-REQUEST RETRIEVAL 0.250 PROCEDURAL CLEAR-BUFFER RETRIEVAL 0.250 DECLARATIVE START-RETRIEVAL 0.250 PROCEDURAL CONFLICT-RESOLUTION 0.250 PROCEDURAL PRODUCTION-SELECTED STOP

Ok, a demo would be easier!

Friday, 2 May 2014

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A Taxonomic Example

  • Let’s consider a familiar (in this class!) task

– Reasoning about categories

  • Their hierarchical relations
  • Their relational structure

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  • strich

animal moves skin fish gills swims shark dangerous swims salmon edible swims bird wings flies canary yellow sings can't fly tall From http://act-r.psy.cmu.edu/tutorials/

Friday, 2 May 2014

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(p1 ISA property object shark attribute dangerous value true) (p2 ISA property object shark attribute locomotion value swimming) (p3 ISA property object shark attribute category value fish) ... (p7 ISA property object fish attribute breathe value gills) (p8 ISA property object fish attribute locomotion value swimming) (p9 ISA property object fish attribute category value animal)

Taxonomy: Declarative

15 (chunk-type property

  • bject

attribute value) (chunk-type is-member

  • bject

category judgment)

  • strich

animal moves skin fish gills swims shark dangerous swims salmon edible swims bird wings flies canary yellow sings can't fly tall

(g1 ISA is-member object canary category bird judgment nil) (g2 ISA is-member object canary category animal judgment nil) (g3 ISA is-member object canary category fish judgment nil) (p1 ISA property

  • bject shark

attribute dangerous value true) (g1 ISA is-member

  • bject canary

category bird judgment nil) From http://act-r.psy.cmu.edu/tutorials/

Friday, 2 May 2014

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Taxonomy: Setup

16 From http://act-r.psy.cmu.edu/tutorials/

Friday, 2 May 2014

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Taxonomy: Procedural -- Direct

17 From http://act-r.psy.cmu.edu/tutorials/

Friday, 2 May 2014

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Taxonomy: Procedural -- Indirect

18 From http://act-r.psy.cmu.edu/tutorials/

Friday, 2 May 2014

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Difference from a DL

  • Unsophisticated representation

– Simple hierarchy – No “class” expressions

  • Unclear represenation

– What’s the semantics? – Does this mean that

  • The shark “is” dangerous?

– Is dangerous a category?

  • Do sharks “have” locomotion?
  • We build the reasoner!

– And it’s not very efficient – It’s not supposed to be efficient per se – It’s supposed to work as we do!

  • A model of human behavior

19 (p1 ISA property

  • bject shark

attribute dangerous value true) (p2 ISA property

  • bject shark

attribute locomotion value swimming)

Friday, 2 May 2014

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ACT-R Development

20 http://act-r.psy.cmu.edu/about/

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Validation

21 http://act-r.psy.cmu.edu/about/

Friday, 2 May 2014

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Applications

22 http://act-r.psy.cmu.edu/about/

Friday, 2 May 2014

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Evaluating ACT-R as KR

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Role 1: Surrogacy Role 2: Ontological Commitment Role 3: Theory of Reasoning Role 4: Efficient Computation Role 5: Human Communication Models Models “Symbols” Global States Objects in relations Buffers, goals, “modules” Deduction Deduction Production rules on limited buffers TTs are hard! Includes Prop N/A?? Hard to say what we mean? Too many ways to say the same thing? ??

Prop Pred ACT-R

Friday, 2 May 2014

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Some readings

  • A brief (dated) survey

– http://act-r.psy.cmu.edu/papers/136/136.pdf

  • The software download includes the tutorial

– http://act-r.psy.cmu.edu/actr6/

  • “An Integrated Theory of Mind”

– http://act-r.psy.cmu.edu/papers/526/FSQUERY.pdf

  • “Tracking Children’s Mental States While Solving

Algebra Equations”

– http://act-r.psy.cmu.edu/papers/905/21391_fta.pdf – Goes with the video

  • http://act-r.psy.cmu.edu/actrnews/index.php?id=34

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Friday, 2 May 2014