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What are agents anyway?

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Overview

• Agents

• Agent environments

• Intelligent agents

• Agents versus objects

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Agent – Wooldridge & Jennings

A computer system that is situated in some environment and is capable of autonomous action in its environment to meet its design

objectives.

Learning?

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Agent – Russell & Norvig

• An agent is anything that can be viewed as– Perceiving its environment– Acting upon that environment

• What is the difference between the two definitions???

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Autonomy

agents operate

without the direct intervention of humans or

other agents, and have some kind of

control over their actions and internal state

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Agent and Environment

Agent Enviro

nment

Sensors

Effectors

autonomousprocessing

ActionOutput

SensorInput

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Intelligent Agents

• Intelligent agents require flexible autonomous action

• Requires the following attributes

– reactivity

– pro-activity

– social ability

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Reactive

agents perceive their environment and

respond, in a timely fashion, to changes

that occur in it

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Pro-active

agents do not simply react to their

environment, they are able to exhibit

goal-directed behavior by taking the

initiative

Key is a balance between goal-directedand reactive behavior!

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Social

agents interact with other agents, and

possibly humans, via an agent-

communication language

Social ability implies coordinationcooperation versus competition

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Example/Non-Example

• Information seeking agent– The user tells the agent what type of

information is desired.– The agent goes to known web sites, databases,

and other sources (including other agents) to collect information about the desired subject.

– After collecting the data, the agent “fuses” the information into a succinct report and returns it to the user.

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Example/Non-Example

• Print agent– The user submits documents to the agent and tells it

what printer to use.

– The agent takes the user’s document, as well as documents from other users, orders them by size and prints them one by one.

– The agent returns the current status of the document and printer when asked by the user.

– The agent tells the user when the document has been printed.

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Example/Non-Example

• Contract Manager agent– The user gives the agent a task to perform– The manager agent then sends the task to a group of

contractor agents for bid– If the contractor agents want to bid, they submit a bid

on the task– The manager agent waits for a time and then selects the

best bid submitted by potential contractor agents– Upon completion, the manager agent notifies the user

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Agents vs. Objects

• Aren’t agents just like objects?

• YES– Computational entities– Encapsulate state– Perform actions– Communicate via message passing

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Agents vs. Objects

• Aren’t agents just like objects?

• NO– Agents embody a strong notion of autonomy– Agents are capable of flexible behavior– Agents operate in their own thread of control

Objects : invokeAgents : request

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Agents vs. Objects

• Can’t you build OO systems with these characteristics?– Yes, but you have built a multiagent system

using object-oriented tools and techniques!– That makes agents a specialization of the

general notion of an object

An active object is one that encompasses its own thread of control […]. Active objects are generally autonomous, meaning that they can exhibit some behavior without being operated upon by another object. Passive objects on the other hand, can only undergo a state change when explicitly acted upon. G. Booch

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Programming progression

• Programming has progressed through:

– machine code– assembly language– machine-independent programming languages– sub-routines– procedures & functions– abstract data-types– objects

to – agents

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Agents vs. Expert systems

Expert systems

• do not directly act on environments, act as consultants.

• input not via sensors, but via users, who act as middle man

• cooperation? maybe backboards

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Summary

• Agents are capable of autonomous action in its environment to meet its design objectives

• Intelligent agents require flexible autonomous action to meet its objectives

• Agents are a specialization of objects that are autonomous, flexible, and operate in their own thread of control

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Agent Architectures

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Overview

• Abstract Architectures– State, Action, Perception– Reflexive agents, Agents with state

• Concrete Architectures– Logic– Reactive– Belief-Desire-Intention– Layered

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Abstract Architectures

• Formalize abstract view

– S = {s1, s2, …} – environment states

– A = {a1, a2, …} – set of possible actions

• Allows us to view an agent as a functionaction : S* A

• Interactions of agent and environment – history

a1 a2 a3 a4 an an+1 h : s0 s1 s2 s3 … sn …

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Abstract Architecture

action

Environment

actionsstates

action

env : S x A (S)action : S* A

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Perceiving State

• Agent can’t see state directly– Requires perception

see : S* P

• And action is actually based on perceptions, not state

action : P* A

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Perceiving State

Environment

actionsstates see action

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• Reflexive agents decide what to do without regard to history – purely reflexive

action : P A

• Example - thermostat

off if temp = okaction(s) =

on otherwise

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Agents with State

• How do we represent the history of percepts?– Sequence of perceptions is unintuitive and

computationally inefficient An agent can maintain its perception of

the current state of the environment Equivalent to history of perceptions Requires more machinery

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Abstract States

• Represent new construct - state

I = {i1, i2, i3 …} – set of internal states

• We now map internal state to actions

action : I A

• Update internal state

next : I x P I

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Agents with State

Environment

actionsstates

see action

next state

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Concrete Architectures

• Logic-Based Architectures

• Reflexive Architectures

• Belief-Desires-Intentions Architectures

• Layered Architectures

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See and Next Functions

• See and next function stay basically the same

see : S P

next : D x P D

Internal state = set of logical expressions

e.g. In(x,y) Dirt(x,y) Facing(d)

What the agentbelieves is true

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• Model decision making by a set of deduction rules for inference

action : D A

• Use logical deduction to try to prove the next action to take, given the current state. If no action can be proven, select an action that is consistent with the rules and database

Logical Decision Making

In(x,y) &Dirt(x,y) -> Do(suck)In(0,0) & notDirt(0,0) & Facing(north) -> Do(forward)In(0,1) & notDirt(0,1) & Facing(north) -> Do(forward)In(0,2) & notDirt(0,2) & Facing(north) -> Do(turn_right)

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• Pro– Simple, elegant, logical semantics

• Con– Computational complexity– Representing the real world– not applicable in (fast) changing environments

Logical Agency Summary

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Reactive Architectures

• Reactive Architectures do NOT use– symbolic world model

– symbolic reasoning

• Three key ideas of reactive agents (Brooks)– raw sensor data - representation of data is close to sensor

data, not symbolic (physical-symbol grounding hypothesis)– emergent functionality - no a priori specification of

behavior– task decomposition - composed of a collection of

autonomous modules, each responsible for a single task

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Brook’sSubsumption Architecture

Explore

Wander

Avoid Obstacles

Sensing

Acting

Originally developed to control RobotsHierarchy of tasksTasks “compete” to exercise controlLower levels represent more primitive behaviorsLower levels have precedence

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Subsumption architectuur

• A set of behaviors, one for each task.– A behavior is:

• ‘situation action’ rule, or

• a neural network, or ...

– A direct link between situation and action.

• An ordering on behaviors.– The subsumption hierarchies.– Lower levels inhibit higher levels.

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Layered behaviors

sensors behavior 0

behavior 1

behavior 2

::

actions

s

s

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Limitations

• Suppose the robot wants to go to the goal. Can we do this with the subsumption architecture?

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emergent behavior

Pros & Cons

• Pros– Simplicity, computationally tractable, robust,

elegance

• Cons– Sufficiency of local information is needed– No accounting for non-local/long term effects– How to do learning

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Belief-Desires-Intentions

• Rooted in practical reasoning– Deciding what goals to achieve– Deciding how to achieve those goals

• BDI– Beliefs = current state

– Desires = possible goals

– Intentions = chosen goals

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BeliefDesires

Intentions

Beliefs

beliefrevision

generateoptions

Desires

filter Intentions

actionsee

Environment

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BDI Pros & Cons

• Pros– Intuitive– Clear functional decomposition

• Cons– Efficiency– Commitment vs. reconsideration

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Layered Architectures

• Based on separating reactive, pro-active and social behavior– May be other layers

• Layering– Horizontal

• I/O connected to each layer

– Vertical• I/O dealt with by single layer

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TouringMachines

• Reactive layer: fast action.• Planning layer: pro-active; no planning but

predefined plans.• Modelling layer: generates goals.

Modeling layer

Planning layer

reactive layer

controle

perception action

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Layered Pros & Cons

• Pros– Most popular– Natural decomposition for social, reactive, and

pro-active behavior

• Cons– Lack conceptual and semantic clarity– Complex interactions between layers

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Summary

• Abstract Architectures– State, Action, Perception– Reflexive agents, Agents with state

• Concrete Architectures– Logic– Reactive– Belief-Desire-Intention– Layered