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Multi-Agent Systems. University “Politehnica” of Bucarest Spring 2011 Adina Magda Florea http://turing.cs.pub.ro/mas_11 curs.cs.pub.ro. Lecture 2: Agent architectures. Simple agent models Cognitive agent architectures Reactive agent architectures Layered architectures. - PowerPoint PPT Presentation
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Multi-Agent Systems
University “Politehnica” of BucarestSpring 2011
Adina Magda Florea
http://turing.cs.pub.ro/mas_11curs.cs.pub.ro
Lecture 2: Agent architectures
Simple agent models Cognitive agent architectures Reactive agent architectures Layered architectures
An agent perceives its environment through sensors and acts upon the environment through effectors.
Aim: design an agent program = a function that implements the agent mapping from percepts to actions.
Agent = architecture + program
A rational agent has a performance measure that defines its degree of success.
A rational agent has a percept sequence and, for each possible percept sequence the agent should do whatever action is expected to maximize its performance measure.
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1. Simple agent models
E = {e1, .., e, ..}P = {p1, .., p, ..}A = {a1, .., a, ..}
Reactive agentsee : E Paction : P Aenv : E x A E(env : E x A P(E))Environment
env (E)
Decisioncomponent
action
Executioncomponent
action
Perceptioncomponent
see
Agent
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P A
Reactive agent modelReactive agent model
Several reactive agentsseei : E Pi
actioni : Pi Ai
env : E x A1 x … An P(E)
Environmentenv
Decisioncomponent
action
Executioncomponent
action
Perceptioncomponent
see
Agent (A1)
Agent (A2)
Agent (A3)
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A1,…, Ai,..P1,…, Pi,..(usually the same)
Reactive agent modelReactive agent model
E = {e1, .., e, ..}P = {p1, .., p, ..}A = {a1, .., a, ..}S = {s1, .., s, ..}
State Agentsee : E Pnext : S x P Saction : S Aenv : E x A P(E)Environment
env (E)
Decisioncomponent
action, next
Executioncomponent
action
Perceptioncomponent
see
Agent
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P A
S
Cognitive agent modelCognitive agent model
Several cognitive agentsseei : E Pi
nexti : Si x P Si
actioni : Si x I Ai
interi : Si I env : E x A1 x … An P(E)
Environmentenv
Decisioncomponent
action, next
Executioncomponent
action
Perceptioncomponent
see
Agent (A1)
Agent (A2)
Agent (A3)
Interactioncomponent
inter
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S1,…, Si,..A1,…, Ai,..P1,…, Pi,..(not always the same)
I = {i1, .., ik,…}
Cognitive agent modelCognitive agent model
Agent with states and goals
goal : E {0, 1}
Agents with utility
utility : E R
Nondeterministic environment
env : E x A P(E)
Utility theory = every state has a degree of usefulness, to an agent, and that agent will prefer states with higher utility
Decision theory = an agent is rational if and only if it chooses the actions that yields the highest expected utility, averaged over all possible outcomes of actions - Maximum Expected Utility 8
Cognitive agent modelCognitive agent model
The expected probability that the result of an action (a) executed in e to be the new state e’
The expected utility of a action a in a state e, from the point of view of the agent
Maximum Expected Utility (MEU)
prob(ex(a, e) e') 1e'env( e,a)
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Cognitive agent modelCognitive agent model
),('
)'(*)'),((),(aeenve
eutilityeeaexprobeaU
Example: getting out of a maze
– Reactive agent
– Cognitive agent
– Cognitive agent with utility
3 problems:
– what action to choose if several available
– what to do if the outcomes of an action are not known
– how to cope with changes in the environment10
2. Cognitive agent architectures
Rational behaviour: AI and Decision theory AI = models of searching the space of possible actions
to compute some sequence of actions that will achieve a particular goal
Decision theory = competing alternatives are taken as given, and the problem is to weight these alternatives and decide on one of them (means-end analysis is implicit in the specification of competing alternatives)
Problem = deliberation/decision– The agents are resource bounded
– The environment may change during deliberation
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General cognitive agent architectureGeneral cognitive agent architecture
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Information aboutitself- what it knows- what it believes- what is able to do- how it is able to do- what it wantsenvironment andother agents- knowledge- beliefs
Communication
Interactions
Control
Output
Input
Otheragents
Environment
Scheduler&Executor State
Planner
Reasoner
FOPL models of agency Symbolic representation of knowledge + use inferences in FOPL -
deduction or theorem proving to determine what actions to execute
Declarative problem solving approach - agent behavior represented as a theory T which can be viewed as an executable specification
(a) Deduction rules
At(0,0) Free(0,1) Exit(east) Do(move_east)
Facts and rules about the environment
At(0,0) Wall(1,1) x y Wall(x,y) Free(x,y)
Automatically update current state and test for the goal state
At(0,3) or At(3,1)
(b) Use situation calculus =describe change in FOPL
Function Result(Action,State) = NewState
At((0,0), S0) Free(0,1) Exit(east) At((0,1), Result(move_east,S0))
Try to prove the goal At((0,3), _) and determines actions that lead to it
- means-end analysis 13
Advantages of FOPL
- simple, elegant
- executable specifications
Disadvantages
- difficult to represent changes over time
other logics
- decision making is deduction and selection of a strategy
- intractable
- semi-decidable
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BDI (Belief-Desire-Intention) architectures
High-level specifications of an architecture for a resource-bounded agent.
Beliefs = information the agent has about the world
Desires = state of affairs that the agent would wish to bring about
Intentions = desires the agent has committed to achieve
BDI - a theory of practical reasoning - Bratman, 1988
Intentions play a critical role in practical reasoning - limits options, DM simpler
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BDI particularly compelling because: philosophical component - based on a theory of
rational actions in humans software architecture - it has been implemented and
successfully used in a number of complex fielded applications– IRMA (Intelligent Resource-bounded Machine Architecture)– PRS - Procedural Reasoning System
logical component - the model has been rigorously formalized in a family of BDI logics– Rao & Georgeff, Wooldrige– (Int i ) (Bel i )
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BDI ArchitectureBelief revision
BeliefsKnowledge
Deliberation process
percepts
DesiresOpportunityanalyzer
Intentions
Filter
Means-endreasonner
Intentions structured in partial plans
PlansLibrary of plans
Executor
B = brf(B, p)
I = options(D, I)
I = filter(B, D, I)
= plan(B, I)
actions
BDI Agent control loopBDI Agent control loop
B = B0 I = I0 D = D0
while true do
get next perceipt p
B = brf(B,p)
D = options(B, D, I)
I = filter(B, D, I)
= plan(B, I)
execute()
end while18
Commitment strategies The option chosen by the agent as an
intention = the agent is committed to that intention
Commitments imply temporal persistence of intentions
Question: How long is an agent committed to an intention
Blind commitment Single minded commitment Open minded commitment
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B = B0
I = I0 D = D0
while true do
get next perceipt p
B = brf(B,p)
D = options(B, D, I)
I = filter(B, D, I)
= plan(B, I)
while not (empty() or succeeded (I, B)) do
= head()
execute()
= tail()
get next perceipt p
B = brf(B,p)
if not sound(, I, B) then
= plan(B, I)
end while
end while 20
Reactivity, replan
BDI Agent control loopBDI Agent control loopBlind commitment
B = B0
I = I0 D = D0
while true do
get next perceipt p
B = brf(B,p)
D = options(B, D, I)
I = filter(B, D, I)
= plan(B, I)
while not (empty() or succeeded (I, B) or impossible(I, B)) do
= head()
execute()
= tail()
get next perceipt p
B = brf(B,p)
if not sound(, I, B) then
= plan(B, I)
end while
end while 21
Reactivity, replan
Dropping intentions that are impossibleor have succeeded
BDI Agent control loopBDI Agent control loopSingle minded commitment
B = B0
I = I0 D = D0
while true doget next perceipt pB = brf(B,p)D = options(B, D, I)I = filter(B, D, I) = plan(B, I)while not (empty() or succeeded (I, B) or impossible(I, B)) do
= head()execute()
= tail()get next perceipt pB = brf(B,p)
D = options(B, D, I) I = filter(B, D, I)
= plan(B, I)end while
end while 22
Replan
if reconsider(I, B) then
BDI Agent control loopBDI Agent control loopOpen minded commitment
BDI architecture – most popular There is no unique BDI architecture
PRS - Procedural Reasoning System (Georgeff) dMARS UMPRS si JAM – C++ JADE (http://jade.tilab.com/ ) JACK (http://aosgrp.com/index.html)
JADEX – XML si Java (http://vsis-www.informatik.uni-hamburg.de/projects/jadex/) JASON – Java (http://jason.sourceforge.net/)
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3. Reactive agent architectures
Subsumption architecture - Brooks, 1986 DM = {Task Accomplishing Behaviours} A TAB is represented by a competence module (c.m.) Every c.m. is responsible for a clearly defined, but not
particular complex task - concrete behavior The c.m. are operating in parallel Lower layers in the hierarchy have higher priority and are
able to inhibit operations of higher layers The modules located at the lower end of the hierarchy are
responsible for basic, primitive tasks The higher modules reflect more complex patterns of
behaviour and incorporate a subset of the tasks of the subordinate modules subsumtion architecture
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Module 1 can monitor and influence the inputs and outputs of Module 2
M1 = wonders about while avoiding obstacles M0M2 = explores the environment looking for
distant objects of interests while moving around M1
Incorporating the functionality of a subordinated c.m. by a higher module is performed using suppressors (modify input signals) and inhibitors (inhibit output) 25
CompetenceModule (1)Move around
CompetenceModule (0)Avoid obstacles
Inhibitor nodeSupressor node
Sensors
CompetenceModule (2)Investigate env
CompetenceModule (0)Avoid obstacles
EffectorsCompetenceModule (1)Move around
Input(percepts)
Output(actions)
More modules can be added:• Replenishing energy• Optimising paths• Making a map of territory• Pick up and put down objects• Investigate environment• Move around• Avoid obstacles
Behavior (c, a) – pair of condition-action describing behavior
Beh = { (c, a) | c P, a A} R = set of behavior rules
R x R - binary inhibition relation on the set of behaviors, total ordering of R
function action( p: P)var fired: P(R)begin
fired = {(c, a) | (c, a) R and p c}for each (c, a) fired doif (c', a') fired such that (c', a') (c, a) then return areturn null
end 26
1990 - Brooks extends the architecture to cope with a large number of c.m. - Behavior Language
Indirect communication
Advantages of reactive architectures
Disadvantages
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4. Layered agent architectures
Combine reactive and pro-active behavior At least two layers, for each type of behavior
Horizontal layering - i/o flows horizontally
Vertical layering - i/o flows vertically
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Layer n
…
Layer 2
Layer 1
perceptualinput
Actionoutput
Layer n
…
Layer 2
Layer 1
Layer n
…
Layer 2
Layer 1
Actionoutput
Actionoutput
perceptualinput
perceptualinput
HorizontalVertical
Layered agent architectures
TouringMachine Horizontal layering - 3 activity producing layers, each layer produces
suggestions for actions to be performed reactive layer - set of situation-action rules, react to precepts from the
environment planning layer
- pro-active behavior
- uses a library of plan skeletons called schemas
- hierarchical structured plans refined in this layer modeling layer
- represents the world, the agent and other agents
- set up goals, predicts conflicts
- goals are given to the planning layer to be achieved Control subsystem
- centralized component, contains a set of control rules
- the rules: suppress info from a lower layer to give control to a higher one
- censor actions of layers, so as to control which layer will do the actions30
InteRRaP Vertically layered two pass agent architecture Based on a BDI concept but concentrates on the dynamic control
process of the agent
Design principles the three layered architecture describes the agent using various
degrees of abstraction and complexity both the control process and the KBs are multi-layered the control process is bottom-up, that is a layer receives control
over a process only when this exceeds the capabilities of the layer beyond
every layer uses the operations primitives of the lower layer to achieve its goals
Every control layer consists of two modules:
- situation recognition / goal activation module (SG)
- planning / scheduling module (PS) 31
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Cooperativeplanning layer SG
SG PS
SG PS
PS
Localplanning layer
Behaviorbased layer
World interface Sensors Effectors Communication
Social KB
Planning KB
World KB
actions percepts
InteRRaP
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Beliefs
Social model
Mental model
World model
Situation
Cooperative situation
Local planning situation
Routine/emergency sit.
Goals
Cooperative goals
Local goals
Reactions
Options
Cooperative option
Local option
Reaction
Operational primitive
Joint plans
Local plans
Behavior patterns
Intentions
Cooperative intents
Local intentions
Response
Sensors
Effectors
BDI model in InteRRaPBDI model in InteRRaP
filter
plan
options
PS
SG
Muller tested InteRRaP in a simulated loading area. A number of agents act as automatic fork-lifts that move in the loading
area, remove and replace stock from various storage bays, and so compete with other agents for resources
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