473 Topics
Agents & Environments
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Agents & Environments
Problem Spaces
Search & Constraint Satisfaction
Knowledge Repr’n & Logical Reasoning
Machine Learning
Uncertainty: Repr’n & Reasoning
Dynamic Bayesian Networks
Markov Decision Processes
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© Daniel S. Weld
Types of Agents: Robots
Intelligent Agents
• Have sensors, effectors
• Implement mapping from percept
sequence to actions
percepts
Environment
Agent
Xavier (CMU)
Walking (MIT)
Aibo (Sony)
actions
• Performance Measure
© Daniel S. Weld
Types of Agents: Immobots
! Intelligent buildings
! Autonomous spacecraft
© Daniel S. Weld
Types of Agents: Immobots
• Autonomous spacecraft
• Intelligent buildings
• Softbots
! Jango.excite.com
! Askjeeves.com
! Expert Systems
• Cardiologist
© Daniel S. Weld
Humanoid - Cog (MIT)
© Daniel S. Weld
Rolling Robots
Xavier (CMU)
Driving Robots
MIT Mobile Robot Group
DAWN (CMU Robotics Lab)
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© Daniel S. Weld
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© Daniel S. Weld
Walking Robots
Humanoid Robots
Cog Project (MIT)
Quadraped (MIT Leg Lab)
Flamingo (MIT Leg Lab)
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© Daniel S. Weld
Software Robots
Defn: Ideal rational agent
For each possible percept sequence, does
whatever action is expected to maximize its
performance measure on the basis of evidence
perceived so far and built-in knowledge.''
© Daniel S. Weld
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© Daniel S. Weld
“For each possible percept sequence, does
whatever action is expected to maximize its
performance measure on the basis of evidence
perceived so far and built-in knowledge.''
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• Rationality vs omniscience?
• Acting in order to obtain valuable
information
© Daniel S. Weld
Defn: Autonomy
Implementing ideal rational agent
An agent is autonomous to the extent
that its behavior is determined by
its own experience
• Table lookup agents
• Agent program
! Simple reflex agents
! Agents with memory
Why is this important?
• Reflex agent with internal state
• Goal-based agents
• Utility-based agents
The parable of the dung beetle
© Daniel S. Weld
© Daniel S. Weld
Simple reflex agents
AGENT
Reflex agent with internal state
Sensors
What world was like
what world is
like now
Effectors
Condition/Action rules
AGENT
© Daniel S. Weld
what action
should I do now?
Effectors
© Daniel S. Weld
Goal-based agents
What world was like
How world evolves
AGENT
Sensors
What world was like
what world is
like now
what it’ll be like
if I do acts A1-An
what action
should I do now?
Effectors
How world evolves
what world is
like now
What my actions do
what it’ll be like
if I do acts A1-An
Utility function
AGENT
© Daniel S. Weld
Sensors
How happy
would I be?
what action
should I do now?
Effectors
ENVIRONMENT
Goals
Utility-based agents
ENVIRONMENT
What my actions do
© Daniel S. Weld
what world is
like now
ENVIRONMENT
what action
should I do now?
ENVIRONMENT
Condition/Action rules
How world evolves
Sensors
While driving, what’s the best
policy?
Properties of Environments
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Observability: full vs. partial vs. non
Deterministic vs. stochastic
Episodic vs. nonepisodic
Static vs. … vs. dynamic
Discrete vs. continuous
• Travel agent
• WWW shopping agent
• Coffee delivery mobile robot
© Daniel S. Weld
• Always put blinker on before turning
• Never use your blinker
• Look in mirror, and use blinker only if you
observe a car that can observe you
• What kind of reasoning?
– logical, goal-based, utility-based?
• What kind of agent is necessary?
– reflex, goal-based, utility-based?
© Daniel S. Weld