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Learning in Worlds with Objects. Leslie Pack Kaelbling MIT Artificial Intelligence Laboratory With Tim Oates, Natalia Hernandez, Sarah Finney. What is an Agent?. A system that has an ongoing interaction with an external environment household robot factory controller web agent - PowerPoint PPT Presentation
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NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 1
Learning in Worlds with Objects
Leslie Pack Kaelbling
MIT Artificial Intelligence Laboratory
With Tim Oates, Natalia Hernandez, Sarah Finney
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 2
What is an Agent?
A system that has an ongoing interaction with an external environment
• household robot• factory controller• web agent• Mars explorer• pizza delivery robot
Environment
ActionObservation
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 3
Agents Must Learn
Learning is a crucial aspect of intelligent behavior• human programmers lack required knowledge• agents should work in a variety of environments• agents should work in changing environments
What to learn?• World dynamics: What happens when I take a
particular action?• Reward: What world states are good?
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 4
Current state-of-the-art learning methods will not work in domains with multiple objects:
These are crucial domains for robots of the future.
Crisis
?
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 5
Representation
Learning requires some sort of representation of states of the world.
The choice of representation affects• what information can be represented• what kinds of generalizations the agent can make
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 6
Attribute VectorState-of-the-art representation for learning
temperature = 48.2pressure = 57.9 mBvalve1 = openvalve2 = closedtime = 10:48AMbacklog = 78volume = 32.2production = 45.5…
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 7
Generalization over Attribute Vectors
0
1
2
3 0
1
2
3
-1-0.5
00.51
0
1
2
3
temp > 22
time < 10AMpressure < 3
closevalve
increasetemp
addreagent
openvalve
temp
time
x
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 8
Complex Everyday Domains
book1-on-book2: truebook2-on-book1: falsepen-is-yellow: truepen-is-blue: falselamp-on: truelamp-off: falseink-bottle-level: 50%lamp-in-bottle: falsebottle-on-lamp: falsepaper1-color: graypaper2-color: whitefabric-behind-lamp: truebook2-is-clear: falsebook4-is-clear: falsebook1-is-clear: trueblock1-on-block2: falseblock3-unstable: trueblock2-on-table: falseblock1-in-front-of-lamp: true
…
Attribute vector is impossibly big
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 9
Generalization over Objects
• If book1 is on book2 and I move book2, then book1 will move
• If the cup is on the table and I move the table, then the cup will move
• If the pen is on the paper and I move the paper, then the pen will move
• If the coat is on the chair and I move the chair, then the coat will move
For all objects A and B:
If A is on B and I move B, then A will move
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 10
Referring to Objects
Traditional symbolic AI has the problem of “symbol grounding”:
How do I know what object is named by book1?
on(book1,book2)
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 11
Deictic Expressions“Deixis” is Greek for “pointing”
koko ima
watashi-ga motteiru hako watashi-ga miteiru hako
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 12
Automatic Generalization
If I have an object in my hand and I open my hand, then the object that was in my hand is now on the table
This is true, no matter what object is in your hand.
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 13
Communicating with Humans
Natural language communication• speaks of the world in terms of objects and their
relationships• uses deictic expressions
Our robots of the future will have to be able to understand and generate human descriptions of the world
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 14
Long-Term Research Goal
A robotic system with hand and cameras that can• learn to achieve tasks efficiently through trial and
error• acquire natural language descriptions of the
objects and their properties through “conversation” with humans
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 15
Short-Term Research Plan
Explore deictic, object-based representation for learning algorithms
• build simulated hand-eye robot system that manipulates blocks (with real physics)
• have simulated robot learn to carry out tasks from trial and error
Demonstrate empirically and theoretically that deictic representation is crucial for efficient learning
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 16
First Example Domain
Unreliable block stacking:• robot is rewarded for making tall piles of blocks• the taller a pile is, the more likely it is to fall over
when another block is added• a pile can be made more stable by building piles to
its sides
Once the robot learns to do this task, keep the physics of the domain the same, but reward a more complex behavior.
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 17
Learning by Doing
Having an initial task to perform focuses the robot’s attention on aspects of the environment
• Use extension of Utree learning algorithm to select important aspects of the environment
• Generate new deictic expressions dynamically: the-block-on-top-of(the-block-I-am-looking-at)
• Extend reinforcement learning methods to apply to object-based representations
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 18
Extracting General Rules
There are too many facts that are true in any interesting environment.
Solving tasks focuses attention on • particular objects (named with deictic
expressions)• particular properties of those objects
These objects and properties are likely of general importance: use them as input to association-rule learning algorithm to learn facts like:
The thing that is on the thing that I am holding will probably fall off if I move
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 19
Enabling Planning
Given general rules, the agent can “think” about the consequences of its actions and decide what to do, rather than learn through trial and error.
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 20
In Future
An ambitious research project• vision algorithms for learning segmentation and
object recognition• learning good properties and relations for
characterizing the domain (“concept learning”)• connect with natural language learning for word
meanings
NTT-MIT Collaboration Meeting, 2001Leslie Pack Kaelbling 21
Don’t missany dirt!