Transcript Intelligent Agents
Intelligent Agents Devika Subramanian Comp440 Lecture 1
Intelligent Agents An agent is anything that can be viewed as perceiving its environment through its sensors and acting upon that environment through its effectors.
agent effectors environment sensors Objective: design agents that perform well in their environments
Informal agent descriptions Thermostat Percepts: temperature sensor Actions: open/close valve Performance measure: maintaining user-set temperature Environment: room/house Internet Newsweeder Percepts: words, bitmaps Actions: word vector counts, cosine transforms, etc Performance measure: retrieving relevant news posts Environment: Internet newsgroups
Specifying performance measures How do we measure how well an agent is doing?
External performance measure or self evaluation?
When do we measure performance of agent?
Continuous, periodic or one-shot evaluation?
Specifying performance measures formally Performance measures are external.
The environment provides feedback to the agent in the form of a function mapping the environment’s state history to a real number.
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* Performance feedback can be provided after each move, periodically, or at the very end.
Example of performance measure for thermostat States of the environment s 0 s 1 s n-1 s n The ambient temperature is being sampled at periodic intervals.
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DESIRED
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Ideal rational agent An ideal rational agent performs actions that are expected to maximize its performance measure on the basis of the evidence provided by its percept sequence and whatever built-in knowledge the agent has.
An ideal rational agent is not omniscient.
Doing actions to gather information is part of rational behavior.
Rationality of agents judged using performance measure, percept sequence, agent’s knowledge, actions it can perform.
Abstract specification of agents Specifying which action an agent ought to take in response to any given percept sequence provides a design for an ideal rational agent f : P* A
Example: a thermostat percepts sensed temperature action A = {no-op,close valve, open valve} Agent function f: T -> A, where T is set of possible ambient temperatures.
What assumptions about the environment and the device are we making with such an agent function?
Agent programs An implementation of the given agent specification f : P* A function thermostat (temperature) If temperature < DESIRED – epsilon return close valve If temperature > DESIRED + epsilon return open valve Return no-op
Autonomous agents An agent is autonomous to the extent that its behavior is determined by its own experience.
Given sufficient time and perceptual information, agent should adapt to new situations and calculate actions appropriate for those situations.
Is a thermostat autonomous?
Is the GPS route planner in your car autonomous?
Taxonomy of agent programs Agents with no internal state Reflex agents or stimulus-response agents Agents with internal state Agents with fixed policies or reflex agents with state (agents that remember the past) Agents that compute policies based on goals or general utility functions (agents that remember the past and can project into the future)
Agent program structure Template for agent programs Function agent ( percept ) returns action local state: l l = update-local-state(l,percept) action = choose-best-action(l) l = update-local-state(l,action) return action
Reflex agents condition action rules Current percept Interpret current state Compute current action by choosing matching condition action rule Action
Template for a reflex agent Function reflexAgent(percept) static : rules, a set of condition-action rules s =interpret-input(percept) rule = find-matching-rule(s,rules) action = rule-action(rule) return action Does not maintain perceptual history!
h(t) An example of a reflex agent (t) 10 o Sensors : h(t), (t) Actions to left : no-op, turn inflow valve to the right, turn inflow valve Performance measure (h(t)-3) 2 : maintain height h(t) at 3 meters (minimize the sum of over t in [0..T])
Condition-action rules h 0-1.5
1.5-2.5
2.5-2.8
2.8-3.0
3.0-3.2
3.2-4.0
0-10 10-15 15-20 20-30 30-60 60-180 right right right right right right right right right noop right right right right noop left left left left noop left left left left left left left left left left noop left left left left left
A reflex agent in nature percept If small moving object then activate SNAP If large moving object then activate AVOID and inhibit SNAP one of SNAP or AVOID
Ralph: Vision based vehicle steering sampling the image of roadway ahead of vehicle determining the road curvature assessing the lateral offset of the vehicle relative to the lane center commanding a steering action computed on the basis of curvature and lane position estimates
Ralph’s sampling strategy
Curvature hypotheses
Curvature scoring
Lateral offset calculation
No hands across America 2850 mile drive from Washington DC to San Diego, on highways.
Trip challenges: driving at night, during rain storms, on poorly marked roads, through construction areas.
Evaluation metric: percent of total trip distance for which Ralph controlled the steering wheel.
Ralph steered the vehicle for 2796 of the 2850 miles (98.1 percent) 10 mile stretch of new, unpainted highway (no lane markers) city driving when road markings were either missing or obscured by other vehicles.
Challenging highway driving
Challenging city roads
Want Ralph in your car?
Fixed video camera, forward-looking, mounted on rear-view mirror inside vehicle.
steering actuator (converts output of Ralph to steering command for vehicle).
now commercially available from Assistware Technology Inc. ($1975 from http://www.assistware.com)
Reflex agents with internal state Current percept Interpret current state Compute current action by choosing matching condition action rule Internal State model of actions and env Action
Example of reflex agent with state An automatic lane changer: need internal state to monitor traffic in lanes unless car has cameras in the front and rear. Internal state allows one to compensate for lack of full observability.
Why internal state is useful Or, why is remembering the past any good?
So, you are not doomed to repeat it (Santayana).
Past + knowledge of actions can help you reconstruct current state --- helps compensate for lack of, or errors in, sensory information.
Template for reflex agent with state Function reflex-agent-with-state (percept) returns action static: state, rules state = update-internal-state(state,percept) rule = rule-match(state,rules) action = rule-action(rule) state = update-internal-state(state,action) return action
The NRL Navigation Task
The NRL Navigation Task
A near-optimal player A three-rule deterministic controller solves the task!
The only state information required is the last turn made.
A very coarse discretization of the state space is needed: about 1000 states!
Discovering this solution was not easy!
Rule 1: Seek Goal There is a clear sonar in the direction of the goal.
If the sonar in the direction of the goal is clear, follow it at speed of 20, unless goal is straight ahead, then travel at speed 40.
Rule 2: Avoid Mine There is a clear sonar but not in the direction of the goal.
Turn at zero speed to orient with the first clear sonar counted from the middle outward. If middle sonar is clear, move forward with speed 20.
Rule 3: Find Gap There are no clear sonars .
If the last turn was non-zero, turn again by the same amount, else initiate a soft turn by summing the right and left sonars and turning in the direction of the lower sum.
Optimal player in action
Where optimal player loses
Goal-based agents (agents that consider the future) Current percept Interpret current state Internal State models goals project forward by one action from current state Compute current action by picking one that achieves goals Action
Computation performed by a goal-based agent a 1 a 2 a 3 Current state a 4 next states projected from current state and action Actions a 1 and a and actions a 3 2 lead to states that do not achieve the goal, and a 4 do. Hence, choose one of a 3 or a 4.
Goal-based agents Do not have fixed policies; they compute what to do on the fly by assessing whether the action they choose achieves the given (fixed) goals.
Are not restricted to one-step look-ahead.
Are programmed by giving them goals, models of actions, and environment.
Utility-based agents (agents that consider the future) Current percept Interpret current state Internal State models project forward by one action from current state Compute current action by picking one that maximizes utility function Utility function Action
Utility-based agents vs goal based agents Goal-based agents are degenerate cases of utility-based agents. The utility function that goal-based agents use is: U(s 0 s 1 … … s n ) = 1 if s n satisfies goals = 0 otherwise
An extended example: navigation in a Manhattan grid
Case 1 Ideal sensors (robot knows where on grid it is accurately, at all times) Ideal effectors (commanded motions are executed perfectly) Environment: all streets two way, no obstacles.
Goal: get from (x 1 ,y 1 ) to (x 2 ,y 2 ) What kind of agent do you need to achieve this goal?
Solution to case 1 Simple reflex agent suffices.
Fixed policy: dead reckoning Go to (x 1 ,y 2 ) Go to (x 2 ,y 2 ) No need for sensing at all; above policy can be implemented blindly.
Case 2 Ideal sensors (robot knows where on grid it is accurately, at all times) Real effectors (commanded motions are not executed perfectly) Environment: all streets two way, no obstacles.
Goal: get from (x 1 ,y 1 ) to (x 2 ,y 2 ) What kind of agent do you need to achieve this goal?
Solution to Case 2 A simple reflex agent suffices.
Fixed control policy that senses position at every time step Command motion to (x 1 ,y 2 ) Sense position and issue correcting motion commands until robot is within epsilon of (x 1 ,y 2 ) Command motion to (x 2 ,y 2 ) Sense position and issue correcting motion commands until robot is within epsilon of (x 2 ,y 2 )
Case 3 Ideal sensors (robot knows where on grid it is accurately, at all times) Ideal effectors (commanded motions are executed perfectly) Environment: one-way streets and blocked streets, no map. Goal: get from (x 1 ,y 1 ) to (x 2 ,y 2 ) What kind of agent do you need to achieve this goal?
Solution to case 3 Need agent with internal state to remember junctions and options that have already been tried there (so it doesn’t repeat past errors endlessly).
Control algorithm: shorten Manhattan distance to destination whenever possible, backing up only when at a dead end. Back up to last junction with an open choice.
Properties of environments Accessible vs inaccessible : if an agent can sense every relevant aspect of the environment, the environment is accessible. Simple reflex agents suffice for such environments.
Deterministic vs non-deterministic state of the environment is completely determined by the current state and the action selected by the agent, the environment is deterministic. Agents with internal state are necessary for non deterministic environments. : if the next Discrete vs continuous driving is continuous.
: whether states and actions are continuous are discrete. Chess is discrete, taxi
Properties of environments (contd.) Episodic vs non-episodic environment, agent’s experience is divided into episodes. The quality of its action depends just on the episode itself --- subsequent episodes do not depend on what actions occur in previous episodes. Agents that reason about the future are unneeded in episodic environments.
: in an episodic Static vs dynamic : if the environment can change while the agent deliberates, the environment is dynamic for the agent. Time-bounded reasoning needed for dynamic environments.