Information Processing Technology Office

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Information Processing Technology
Office Learning Workshop April 12, 2004 Seedling
Overview Learning Hierarchical Relational
Skills from Reasoning and Experience Institute
for the Study of Learning and Expertise PI Pat
Langley Presenter Ray Mooney
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Learning Objective

• Develop learning methods that operate over rich
  knowledge structures which
• support both reactive control and problem solving
  
• are embedded in an integrated cognitive
  architecture
• that operates in complex physical environments
• Learning mechanisms can acquire and revise such
  knowledge more rapidly and effectively than human
  programmers can create and debug it manually.

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What is Being Learned?

• ICARUS is an integrated cognitive architecture
  that learns
• the logical structure of relational skills and
  concepts
• a hierarchical organization over these elements
• numeric utility functions attached to skills and
  concepts
• that describe effective means for achieving goals
  
• that support reactive control of physical agents
• from background knowledge, experience with
  executing skills in the environment, and problem
  solving
• in an incremental, cumulative manner that
  responds to changes in tasks and the environment.
• This relies on the tight integration of
  execution, problem
• solving, and learning.

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What is Being Learned?

• For example, in a driving domain, ICARUS would
  learn
• the structure of driving skills like turning and
  passing
• the structure of driving concepts (e.g.,
  passable)
• hierarchical connections (e.g., pass and
  change-lanes)
• how to achieve high-level goals (e.g., package
  delivery)
• how to get from one place to another (route
  knowledge)
• the expected utility of driving skills and
  subskills
• the expected utility of driving concepts
• This different content is cast within a unified
  formalism
• that ICARUS provides for encoding knowledge.

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How is Knowledge Being Learned?

• The ICARUS architecture learns
• value functions using a hierarchical variant on
  model-based reinforcement learning
• new skills and concepts based on the cached
  results of means-ends problem solving.
• Learning and reasoning are integrated in that
• conceptual inference and hierarchical skills
  provide high-level descriptions for reinforcement
  learning
• problem-solving traces form the basis of new
  skills and concepts.
• Learning is automatic but could be adapted to
  benefit
• from advice and traces of expert behavior.
• Structure learning occurs from single instances
  value
• learning should be much faster than in typical
  methods.

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How is Knowledge Being Learned?
Means-ends analysis produces hierarchical skills
E
D
C
10
11
8
9
1
B
A
6
7
2
3
5
4
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How is the Knowledge Represented?

• ICARUS casts both background and learned
  knowledge as
• logical relational concepts with linear value
  functions
• logical relational skills with linear value
  functions
• that are defined in terms of other skills and
  concepts.
• Background knowledge constrains the learning of
  value
• functions and provides components for structure
  learning.
• ICARUS provides a formalism for encoding
  knowledge
• about physical domains with continuous attributes
• that includes probability of success, expected
  duration, and resource requirements
• described at multiple levels of abstraction over
  both state (with concepts) and time (with skills).

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How is the Knowledge Represented?
(make-right-turn (?self ?corner)
objective ((behind-right-corner ?corner))
start ((in-rightmost-lane ?self)
(ahead-right-corner ?corner) (at-turning-distanc
e ?corner)) requires ((near-block-corner
?corner) (at-turning-speed ?self))
ordered ((begin-right-turn ?self ?corner)
(end-right-turn ?self ?corner)) value (30.0) )
(slow-for-intersection (?self)
percepts ((self ?self speed ?speed) (corner
?corner street-dist ?dist)) objective ((slow-en
ough-intersection ?self)) requires ((near-block-
corner ?corner) actions ((slow-down))
value ( ( -5.2 ?dist) ( 20.3 ?speed)) )
Some ICARUS driving skills
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How is the Knowledge Represented?
(corner-ahead-left (?corner) percepts ((corner
?corner r ?r theta ?theta)) tests ((lt ?theta
0) (gt ?theta -1.571)) value (( ( 5.6 ?r)
( 3.1 ?theta)) ) (in-intersection (?self)
percepts ((self ?self) (corner ?ncorner
street-dist ?sdist)) positives ((near-block-corn
er ?ncorner) (corner-straight-ahead ?scorner))
negatives ((far-block-corner ?fcorner))
tests ((lt ?sdist 0.0)) value (-10.0) )
Some ICARUS driving concepts
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What is the Domain?

• Our initial studies of ICARUS have focused on a
  simulated
• in-city driving environment that
• requires integration of perception, action, and
  cognition
• involves both reactive control and goal direction
• supports many distinct tasks of varying
  complexity
• provides clear opportunities for cumulative
  learning
• The environment lets us vary domain
  characteristics
• systematically and record statistics on agent
  behavior.
• However, ICARUS aims at broad generality and
  should
• support reasoning and learning in
• both first-person and strategy games
• crisis-response tasks involving physical response
• intelligent assistants for office activities

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How is Progress Being Measured?

• Dependent variables
• Efficiency of task execution (e.g., driving time)
• Quality of task execution (e.g., gas used,
  accidents)
• Higher-order metrics
• Rate and asymptote of learning curves
• Transfer to related tasks and altered
  environments
• Independent variables
• Inclusion or omission of learning methods
• Amount of background knowledge available
• Task difficulty and environmental complexity
• Amount of experience, task/environment similarity

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What are the Technical Milestones?

• Year 1
• Learn estimates of driving skills duration and
  success
• Learn higher-level driving skills via problem
  solving
• Demonstrate improvement on multi-package delivery
• Year 2
• Learn value functions for driving concepts
• Learn trade-offs among many high-level tasks
• Demonstrate transfer and scaling to complex tasks
• Year 3
• Acquisition of place and route knowledge
• Support episodic memory and perceptual attention
• Demonstrate cumulative learning and change
  resilience
• We will also examine other domains to ensure
  generality.