Ontologies Reasoning Components Agents Simulations Architectural Patterns for Agents Jacques Robin.
Ontologies Reasoning Components Agents Simulations Introduction to Intelligent Agents Jacques Robin.
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Transcript of Ontologies Reasoning Components Agents Simulations Introduction to Intelligent Agents Jacques Robin.
OntologiesReasoningComponentsAgentsSimulations
Introduction to Intelligent AgentsIntroduction to Intelligent Agents
Jacques Robin
OutlineOutline
What are intelligent agents? Characteristics of artificial intelligence Applications and sub-fields of artificial intelligence Characteristics of agents Characteristics of agents’ environments Agent architectures
What are Intelligent Agents?What are Intelligent Agents?
Q: What are Software Agents? A: Software which architecture is based on
the following abstractions: Immersion in a distributed environment,
continuous thread, encapsulation, sensor, perception, actuator, action, own goal, autonomous decision making
Q: What is Artificial Intelligence? A: Field of study dedicated to:
Reduce the range of tasks that humans carry out better than current software or robots
Emulate humans’ capability to solve approximately but efficiently most instances of problems proven (or suspected) hard to solve algorithmically (NP-Hard, Undecidable etc.) in the worst case, using innovative, often human inspired, alternative computational metaphors and techniques
Emulate humans’ capability to solve vaguely specified problems using partial, uncertain information
ArtificialIntelligence
Agents
DistributedSystems
SoftwareEngineering
Artificial Intelligence: CharacteristicsArtificial Intelligence: Characteristics
Highly multidisciplinary inside and outside computer science Ran-away field - by definition - at the forefront of computation
tackling ever more innovative, challenging problems as the one it solved become mainstream computing
Most research in any other field of computation also involves AI problems, techniques, metaphors
Q: What conclusions can be derived from these characteristics? A: Hard to avoid; very, very hard to do well
“Well” as in: Well-founded (rigorously defined theoretical basis, explicit simplifying
assumptions and limitations) Easy to use (seamlessly integrated, easy to understand) Easy to reuse (general, extendable techniques) Scalable (at run time, at development time)
What is an Agent?What is an Agent?General Minimal DefinitionGeneral Minimal Definition
Any entity (human, animal, robot, software): Situated in an environment (physical, virtual or simulated) that Perceives the environment through sensors (eyes, camera, socket) Acts upon the environment through effectors (hands, wheels,
socket) Possess its own goals, i.e., preferred states of the environments
(explicit or implicit) Autonomously chooses its actions to alter the environment
towards its goals based on its perceptions and prior encapsulated information about the environment
Processing cycle:1. Use sensor to perceive P2. Interprets I = f(P)3. Chooses the next action A = g(I,G) to perform to reach its goal G4. Use actuator to execute A
What is an Agent? What is an Agent?
AutonomousReasoning
AgentAgent
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Sensors
Effectors
Goals
Action Choice:A = g(I,O)
A
PerceptionInterpretation:
I = f(P)
P 1. Environment percepts2. Self-percepts3. Communicative
percepts
1. Environment altering actions
2. Perceptive actions3. Communicative
actions
Agent x ObjectAgent x Object
Intentionality: Encapsulate own goals (even if implicitly) in addition to data and behavior
Decision autonomy: Pro-actively execute behaviors
to satisfy its goals Can negate request for
execution of a behavior from another agent
More complex input/output: percepts and actions
Temporal continuity: encapsulate an endless thread that constantly monitors the environment
Coarser granularity: Encapsulate code of size
comparable to a package or component
Composed of various objects when implemented using an OO language
No goal
No decision autonomy: Execute behaviors only
reactively whenever invoked by other objects
Always execute behavior invoked by other objects
Simpler input/output: mere method parameters and return values
Temporally discontinuous: active only during the execution of its methods
Intelligent Agent Intelligent Agent x x Simple Software AgentSimple Software Agent
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Goals
Percept Interpretation: I = f(P)
Action Choice: A = g(I,O)
ConventionalProcessing
ConventionalProcessing
AI
AI
Intelligent AgentIntelligent Agent
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Sensors
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Goals
PerceptInterpretation
Action Choice
AI
AI
Situated AgentSituated Agent
Reasoning
InputData
OutputData
Goal
DisembodiedDisembodiedAIAI
SystemSystem
AI
Classical AI Classical AI SystemSystem
What is an Agent? What is an Agent? Other Optional PropertiesOther Optional Properties
Reasoning AutonomyReasoning Autonomy:: Requires AI, inference engine and knowledge base Key for: embedded expert systems, intelligent controllers, robots,
games, internet agents ... AdaptabilityAdaptability::
Requires IA, machine learning Key for: internet agents, intelligent interfaces, ...
SociabilitySociability:: Requires AI + advanced distributed systems techniques:
Standard protocols for communication, cooperation, negotiation Automated reasoning about other agents’ beliefs, goals, plans and
trustfulness Social interaction architectures
Key for: multi-agent simulations, e-comerce, ...
What is an Agent?What is an Agent?Other Optional PropertiesOther Optional Properties
PersonalityPersonality:: Requires AI, attitude and emotional modeling Key for: Digital entertainment, virtual reality avatars,
user-friendly interfaces ... Temporal continuity and persistenceTemporal continuity and persistence::
Requires interface with operating system, DBMS Key for: Information filtering, monitoring, intelligent control, ...
MobilityMobility:: Requires:
Network interface Secure protocols Mobile code support
Key for: information gathering agents, ... Security concerns prevented its adoption in practice
Welcome to the Wumpus World!Welcome to the Wumpus World!
Agent-Oriented Formulation: Agents: gold digger Environment objects:
caverns, walls, pits, wumpus, gold, bow, arrow
Environment’s initial state Agents’ goals:
be alive cavern (1,1) with the gold Perceptions:
Touch sensor: breeze, bump Smell sensor: stench Light sensor: glitter Sound sensor: scream
Actions: Legs effector: forward, rotate 90º Hands effector: shoot, climb out
Wumpus World: AbbreviationsWumpus World: Abbreviations
1
2
3
41 2 3
4
start
S
AB P
W
B
B
S
S, B, G P
P
B
B
GA - AgentW - WumpusP - PitG - GoldX? – Possibly XX! – Confirmed XV – Visited CavernB – BreezeS – StenchG – GlitterOK – Safe Cavern
Perceiving, Reasoning and ActingPerceiving, Reasoning and Actingin the Wumpus Worldin the Wumpus World
Percept sequence:
t=2
1
2
3
41 2 3
4
Aok
ok
ok
t=0
nothing breeze
1
2
3
41 2 3
4
okA
ok
V
okP?
P?
b
Wumpus world model maintained by agent:
1
2
3
41 2 3
4
ok
Aok
V Vbok
W!
s
ok
P!
stench
t=11: Go to (2,3) to find gold
1
2
3
41 2 3
4
ok
A
Sok
V Vbok
P!
W!
Vok
V
S B GP?
P?
ok
t=7: Go to (2,1), Sole safe unvisited cavern
Percept sequence:
Wumpus World Model:
Perceiving, Reasoning and ActingPerceiving, Reasoning and Actingin the Wumpus Worldin the Wumpus World
{stench, breeze, glitter}
Action Sequence:
Classification DimensionsClassification Dimensionsof Agent Environmentsof Agent Environments
Agent environments can be classified as points in a multi-dimensional spaces
The dimensions are: Observability Determinism Dynamicity Mathematical domains of the variables Episodic or not Multi-agency Size Diversity
ObservabilityObservability
Fully observable (or accessible):Fully observable (or accessible): Agent sensors perceive at each instant all the aspects of the
environment relevant to choose best action to take to reach goal Partially observable (or inaccessible, or with hidden variables)Partially observable (or inaccessible, or with hidden variables) Sources of partial observability:Sources of partial observability:
Realm inaccessible to any available sensor Limited sensor scope Limited sensor sensitivity Noisy sensors
Determinism Determinism
Deterministic:Deterministic: all occurrence of executing a given action in a given situation always yields same result
Non-deterministic (or stochastic):Non-deterministic (or stochastic): action consequences partially unpredictable
Sources of non-determinism:Sources of non-determinism: Inherent to the environment: quantic granularity, games with
randomness Other agents with unknown or non-deterministic goal or action
policy Noisy effectors Limited granularity of effectors or of the representation used to
choose the actions to execute
Dynamicity: StaticDynamicity: Staticand Sequential Environments and Sequential Environments
Static: Single perception-reasoning-action cycle during which environment is static
Sequential: Sequence of perception-reasoning-action cycles during each of which the environment changes only as a result of the agent’s actions
Perc
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Static Environment
Agent
Ação
State 1 State 2
Reasoning
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t
Sequential Environment
Agent
Acti
on
State 1
Reasoning
Perc
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Acti
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State 2
Reasoning
Perc
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Ação
State 3
Reasoning
State N...
Synchronous: Environment can change on its own between one action and the next perception of an agent, but not during its reasoning
Asynchronous: Environment can change on its own at any time, including during the agent’s reasoning
Dynamicity: ConcurrentDynamicity: ConcurrentSynchronous and AsynchronousSynchronous and Asynchronous
...P
erc
ep
t
Synchronous ConcurrentEnvironment
Agent
Acti
on
State 1
Reasoning
Perc
ep
t
Acti
on
State 2
Reasoning
State 4 State 5
State 3
...
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t
Asynchronous ConcurrentEnvironment
Agent
Acti
on
State 1
Reasoning
State 2 State 4
State 3
Perc
ep
t
Acti
on
State 5
Reason
ing
State 6
Stationary: The underlying laws or rules that govern state changes in the environment are fixed and immutable; they remain the same during the entire lifetime of the agent ex, a soccer game is asynchronous, yet stationary
Non-Stationary: The underlying laws or rules that govern state changes in the environment are themselves subject to dynamic changes (meta-level changes) during the lifetime of the agent ex, an accounting agent acts in a non-stationary environment, since the tax
laws are subject to changes from one year to the next
Dynamicity: Stationary andDynamicity: Stationary andNon-StationaryNon-Stationary
Multi-AgencyMulti-Agency
Sophistication of agent society: Number of agent roles and agent instances Multiplicity and dynamicity of agent roles Communication, cooperation and negotiation protocols
Main classes: Mono-agent Multi-agent cooperative Multi-agent competitive Multi-agent cooperative and competitive
With static or dynamic coalitions
Mathematical Domain of VariablesMathematical Domain of Variables
Binary Dichotomical
Boolean
Qualitative Nominal
Ordinal
Quantitative Interval
Fractional
Discrete
ContinuousR
[0,1]
MAS variables: Parameters of agent percepts, actions and goals Attributes of environment objects Arguments of environment relations, states, events and locations
Mathematical Domain of VariablesMathematical Domain of Variables
Binary:Binary: Boolean, ex, Male {True,False} Dichotomic, ex, Sex {Male,
Female} Nominal (or categorical)Nominal (or categorical)
Finite partition of set without order nor measure
Relations: only = ou ex, Brazilian, French, British
Ordinal (or enumerated):Ordinal (or enumerated): Finite partition of (partially or
totally) ordered set without measure
Relations: only =, , , > ex, poor, medium, good, excellent
Interval:Interval: Finite partition of ordered set
with measure m defining distance d: X,Y, d(X,Y) = |m(X)-m(Y)|
No inherent zero ex, Celsius temperature
Fractional (or proportional):Fractional (or proportional): Partition with distance and
inherent zero Relations: anyone ex, Kelvin temperature
Continuous (or real)Continuous (or real) Infinite set of values
Other CharacteristicsOther Characteristics
Episodic: Agent experience is divided in separate episodes Results of actions in each episode, independent of previous
episodes ex.: image classifier is episodic, chess is not soccer tournament is episodic, soccer game is not
Open environment: Partially observable, Non-deterministic, Non-episodic, Continuous
Variables, Concurrent Asynchronous, Multi-Agent. ex.: RoboCup, Internet, stock market
Size and DiversitySize and Diversity
Size, Size, i.e.,i.e., number of instances of: Agent percepts, actions and
goals Environment agents, objects,
relations, states, events and locations
Dramatically affects scalability of agent reasoning execution
Diversity, Diversity, i.e., number of classes of: Agent percepts, actions and
goals Environment agents, objects,
relations, states, events and locations
Dramatically affects scalability of agent knowledge acquisition process
Agents’ Internal ArchitecturesAgents’ Internal Architectures
Reflex agent (purely reactive) Automata agent (reactive with state) Goal-based agent Planning agent Hybrid, reflex-planning agent Utility-based agent (decision-theoretic) Layered agent Adaptive agent (learning agent)
Cognitive agent Deliberative agent
Remember … Remember …
Reasoning
AgentAgent
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PerceptInterpretation:
I = f(P)
Action Choice:A = g(I,O)
A
P
So?So?
Goals
Percept Interpretation: I = f(P)
Action Choice:A = g(I,O)
En
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RulesPercepts Action
A(t) = h(P(t))
A
P
Reflex AgentReflex Agent
Principle: Use rules (or functions, procedures) that associate directly percepts
to actions ex. IF speed > 60 THEN fine ex. IF front car’s stop light switches on THEN brake
Execute first rule which left hand side matches the current percepts Wumpus World example
IF visualPerception = glitter THEN action = pick see(glitter) do(pick) (logical representation)
Pros: Condition-action rules is a clear, modular, efficient representation
Cons: Lack of memory prevents use in partially observable, sequential, or
non-episodic environments ex, in the Wumpus World a reflex agent can’t remember which path
it has followed, when to go out of the cavern, where exactly are located the dangerous caverns, etc.
Automata AgentAutomata Agent
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(Past and) CurrentEnviroment Model
Percept InterpretationRules:percepts(t) model(t) model’(t)
Action ChoiceRules:model’’(t) action(t), action(t) model’’(t) model(t+1)
Model UpdateRegras: model(t-1) model(t) model’(t) model’’(t)
Goals
Automata AgentAutomata Agent
Rules associate actions to percept indirectly through the incremental construction of an environment model (internal state of the agent)
Action choice based on: current percepts + previous percepts + previous actions +
encapsulated knowledge of initial environment state Overcome reflex agent limitations with partially observable,
sequential and non-episodic environments Can integrate past and present perception to build rich
representation from partial observations Can distinguish between distinct environment states that are
indistinguishable by instantaneous sensor signals Limitations:
No explicit representation of the agents’ preferred environment states
For agents that must change goals many times to perform well, automata architecture is not scalable (combinatorial explosion of rules)
Automata Agent Rule ExamplesAutomata Agent Rule Examples
Rules percept(t) model(t) model’(t) IF visualPercept at time T is glitter
AND location of agent at time T is (X,Y)THEN location of gold at time T is (X,Y)
X,Y,T see(glitter,T) loc(agent,X,Y,T) loc(gold,X,Y,T).
Rules model’(t) model’’(t) IF agent is with gold at time T
AND location of agent at time T is (X,Y)THEN location of gold at time T is (X,Y)
X,Y,T withGold(T) loc(agent,X,Y,T) loc(gold,X,Y,T).
Automata Agent Rule ExamplesAutomata Agent Rule Examples
Rules model(t) action(t) IF location of agent at time T = (X,Y)
AND location of gold at time T = (X,Y) THEN choose action pick at time T
X,Y,T loc(agent,X,Y,T) loc(gold,X,Y,T) do(pick,T)
Rules action(t) model(t) model(t+1) IF choosen action at time T was pick
THEN agent is with gold at time T+1 T done(pick,T) withGold(T+1).
(Explicit) Goal-Based Agent(Explicit) Goal-Based Agent
Enviro
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(Past and) CurrentEnvironment Model
Percept InterpretationRules: percept(t) model(t) model’(t)
Action ChoiceRules: model’’(t) goals’(t) action(t) action(t) model’’(t) model(t+1)
Model UpdateRules: model(t-1) model(t) model’(t) model’’(t)
Goal UpdateRules: model’’(t) goals(t-1) goals’(t) Goals
(Explicit) Goal-Based Agent(Explicit) Goal-Based Agent
Principle: explicit and dynamically alterable goals Pros:
More flexible and autonomous than automata agent Adapt its strategy to situation patterns summarized in its goals
Limitations: When current goal unreachable as the effect of a single action,
unable to plan sequence of actions Does not make long term plans Does not handle multiple, potentially conflicting active goals
Goal-Based Agent Rule ExamplesGoal-Based Agent Rule Examples
Rule model(t) goal(t) action(t) IF goal of agent at time T is to return to (1,1) AND agent is in (X,Y) at time T AND orientation of agent is 90o at time T AND (X,Y+1) is safe at time T AND (X,Y+1) has not being visited until time T AND (X-1,Y) is safe at time T AND (X-1,Y) was visited before time T THEN choose action turn left at time T X,Y,T, (N,M,K goal(T,loc(agent,1,1,T+N)) loc(agent,X,Y,T) orientation(agent,90,T) safe(loc(X,Y+1),T) loc(agent,X,Y+1,T-M) safe(loc(X-1,Y),T) loc(agent,X,Y+1,T-K)) do(turn(left),T)
Y+1
ok
Yv ok
A
X-1 X
Goal-Based Agent Rule ExamplesGoal-Based Agent Rule Examples
Rule model(t) goal(t) action(t) IF goal of agent at time T is to find gold AND agent is in (X,Y) at time T AND orientation of agent is 90o at time T AND (X,Y+1) is safe at time T AND (X,Y+1) has not being visited until time T AND (X-1,Y) is safe at time T AND (X-1,Y) was visited before time T THEN choose action forward at time T X,Y,T, (N,M,K goal(T,withGold(T+N)) loc(agent,X,Y,T) orientation(agent,90,T) safe(loc(X,Y+1),T) loc(agent,X,Y+1,T-M) safe(loc(X-1,Y),T) loc(agent,X,Y+1,T-K)) do(forward,T)
Y+1
ok
Yv ok
A
X-1 X
Goal-Based Agent Rule ExamplesGoal-Based Agent Rule Examples
Rule model(t) Rule model(t) goal(t) goal(t) goal’(t) goal’(t)//If the agent reached it goal to hold the gold, //then its new goal shall be to go back to (1,1) IF goal of agent at time T-1 was to find gold AND agent is with gold at time T THEN goal of agent at time T+1 is to be in location (1,1) T, (N goal(agent,T-1,withGold(T+N)) withGold(T) M goal(agent,T,loc(agent,1,1,T+M))).
Planning AgentPlanning Agent
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(Past and)Current
EnvironmentModel
Percept InterpretationRules: percept(t) model(t) model’(t)
Action ChoiceRules: model(t+n) = result([action1(t),...,actionN(t+n)] model(t+n) goal(t) do(action1(t))
Model UpdateRules: model(t-1) model(t) model’(t) model’’(t)
Goal UpdateRules: model’’(t) goals(t-1) goals’(t)
GoalsPrediction of Future EnvironmentsRules: model’’(t) model(t+n) model’’(t) action(t) model(t+1)
HypotheticalFuture
EnvironmentModels
Planning AgentPlanning Agent
Percept and actions associated very indirectly through: Past and current environment model Past and current explicit goals Prediction of future environments resulting from different possible
action sequences to execute Rule chaining needed to build action sequence from rules
capture immediate consequences of a single action Pros:
Foresight allows choosing more relevant and safer actions in sequential environments
Cons: little point in building elaborated long term plans in, Highly non-deterministic environment (too many possibilities to
consider) Largely non-observable environments (not enough knowledge
available before acting) Asynchronous concurrent environment (only cheap reasoning can
reach a conclusion under time pressure)
Planning ThreadPlanning Thread
Goals
Current,past and
futureenvironment
model
Current Model Update
Percept Interpretation
Goal Update
Future Environments Prediction
Action Choice
Hybrid Reflex-Planning AgentHybrid Reflex-Planning Agent
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Reflex ThreadReflex ThreadReflex RulesPercepts Actions
Synchronization
Hybrid Reflex-Planning AgentHybrid Reflex-Planning Agent
Pros: Take advantage of all the time and knowledge available to choose
best possible action (within the limits of its prior knowledge and percepts)
Sophisticated yet robust Cons:
Costly to develop Same knowledge encoded in different forms in each component Global behavior coherence harder to guarantee Analysis and debugging hard due to synchronization issues Not that many environments feature large variations in available
reasoning time in different perception-reasoning-action cycles
Layered AgentsLayered Agents
Many sensors/effectors are too fine-grained to reason about goals using directly the data/commands they provide
Such cases require a layered agent that decomposes its reasoning in multiple abstraction layers
Each layer represent the percepts, environment model, goals, and actions at a different level of details
Abstraction can consist in: Discretizing, approximating, clustering, classifying data from prior
layers along temporal, spatial, functional, social dimensions Detail can consist in:
Decomposing higher-level actions into lower-level ones along temporal, spatial, functional, social dimensions
Abstract
Decide Abstractly
Detail
Act in DetailPerceive in Detail
Percept InterpretationPercept Interpretation
Am
bie
nte
Sensors
Effectors
Environment ModelEnvironment ModelEnvironment Model UpdateEnvironment Model Update
Action Choice and Execution ControlAction Choice and Execution Control
Layered Automata Agent
Layer0: f(x).dxy
Layer1: y).P(y)|P(zP(s)
Layer0: f(x).dxy
Layer1: y).P(y)|P(zP(s)
Layer2: q(A)r(B)B)s(A,
Layer2: q(A)r(B)B)s(A,
q(A)r(B)B)s(A, q(A)r(B)B)s(A, Layer2: Layer2:
Utility-Based AgentUtility-Based Agent
Principle: Goals only express boolean agent preferences among environment states A utility function u allows expressing finer grained agent preferences
u can be defined on a variety of domains and ranges: actions, i.e., u: action R (or [0,1]), action sequences, i.e., u: [action1, ..., actionN] R (or [0,1]), environment states, i.e., u: environmentStateModel R (or [0,1]), environment state sequences, i.e., u: [state1, ..., stateN] R (or [0,1]), environment state, action pairs,
i.e., u: environmentStateModel x action R (or [0,1]), environment state, action pair sequences,
i.e., u: [(action1-state1), ..., (actionN-stateN)] R (or [0,1]), Pros:
Allows solving optimization problems aiming to find the best solution Allows trading-off among multiple conflicting goals with distinct
probabilities of being reached Cons:
Currently available methods to compute (even approximately) argmax(u) do not scale up to large or diverse environments
Utility-Based Reflex AgentUtility-Based Reflex Agent
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Percept Interpretation:Rules: percept actions
Goals
Action Choice:Utility Functionu:actions R U(a))argmaxdo(
actionsa
Utility-Based Planning Agent
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Past &Current
EnvironmentModel
Percept InterpretationRegras: percept(t) model(t) modelo’(t)
Model UpdateRegras: model’(t) model’’(t)
Future Environment PredictionRegras: model’’(t) ação(t) model(t+1) model’’(t) model(t+1)
HypothesizedFuture
EnvironmentsModel
Action Choice])))action actionU(result([argmaxdo( i
ni
i1
actioni1
...
Utility Function:u: model(t+n) R
Learning Component
Performance Analysis Component
Adaptive AgentAdaptive Agent
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ActingComponent
New Problem Generation Component
• Reflex• Automata• Goal-Based• Planning• Utility-Based• Hybrid
• Learn rules or functions: • percept(t) action(t)• percept(t) model(t) modelo’(t)• modelo(t) modelo’(t)• modelo(t-1) modelo(t)• modelo(t) action(t)• action(t) model(t+1)• model(t) goal(t) action(t)• goal(t) model(t) goal’(t)• utility(action) = value• utility(model) = value
Simulated EnvironmentsSimulated Environments
Environment simulator: Often themselves internally follow an agent architecture Should be able to simulate a large class of environments that can
be specialized by setting many configurable parameters either manually or randomly within a manually selected range ex, configure a generic Wumpus World simulator to generate world
instances with a square shaped cavern, a static wumpus and a single gold nugget where the cavern size, pit numbers and locations, wumpus and gold locations are randomly picked
Environment simulator processing cycle:1. Compute percept of each agent in current environment2. Send these percepts to the corresponding agents3. Receives the action chosen by each agent4. Update the environment to reflect the cumulative consequences
of all these actions
EnvironmentEnvironmentSimulationSimulation
ServerServer
RedeRede
Environment Simulator ArchitectureEnvironment Simulator Architecture
SimulatedEnvironmentModel
SimulationVisualization
GUIEnvironment UpdateRules: model(t-1) model(t)action(t) model(t-1) model(t)
Percept GenerationRules: model(t) percept(t)
percepts
actionsAgent
Client 1
AgentClient N
...
AI’s PluridisciplinarityAI’s Pluridisciplinarity
Philosophy
Mathematics:• Logic• Probabilities & Statistics• Calculus• Algebra
Psychology(Cognitive)
Economics Sociology
GameTheory
NeurologyZoology
PaleontologyDecisionTheory
LinguisticsOperations Research
InformationTheory
Computer Science:• Theory• Distributed Systems• Software Engineering• Databases
ArtificialIntelligence
AI RoadmapAI Roadmap
Generic Tasks:• Clustering• Classification• Temporal Projection• Diagnosis• Monitoring• Repair• Control• Recommendation• Configuration• Discovery• Design• Allocation• Timetabling• Planning• Simulation
Specific Sub-Fields:• Multi-Agent Communication, Cooperation & Negotiation• Speech & Natural Language Processing• Computer Perception & Vision• Robotic Navigation & Manipulation• Games• Intelligent Tutoring Systems
+ P(A|B)
AI Metaphors, Abstractions
Problem
Generic Sub-Fields:• Heuristic Search• Automated Reasoning & Knowledge Representation• Machine Learning & Knowledge Acquisition• Pattern Recognition
Computational Metaphors:• Algorithmic Exploration• Logical Derivation• Probability Estimation• Connectionist Activation• Evolutionary Selection
Algorithm
Today’s Diversity of AI ApplicationsToday’s Diversity of AI Applications
Agriculture, Natural Resource Management, and the Environment
Architecture & Design Art Artificial Noses Astronomy & Space Exploration Assistive Technologies Banking, Finance & Investing Bioinformatics Business & Manufacturing Drama, Fiction, Poetry, Storytelling
& Machine Writing
Earth & Atmospheric Sciences Engineering Filtering Fraud Detection & Prevention Hazards & Disasters Information Retrieval & Extraction Knowledge Management
Law Law Enforcement & Public Safety Libraries Marketing, Customer Relations & E-
Commerce
Medicine Military Music Networks - including Maintenance,
Security & Intrusion Detection Politics & Foreign Relations Public Health & Welfare Scientific Discovery Social Science Sports Telecommunications Transportation & Shipping Video Games, Toys. Robotic Pets &
Entertainment
AI Pays !AI Pays !
AI Industry Gross Revenue: 2002: US $11.9 billions Annual growth rate: 12.2% Projection for 2007: $21.2 billions www.aaai.org/AITopics/html/stats.html
Companies specialized in AI: http://dmoz.org/Computers/Artificial_Intelligence/Companies/
Corporations developing and using AI: Google, Amazon, IBM, Microsoft, Yahoo, ...
Corporations using IA: www.businessweek.com/bw50/content/mar2003/a3826072.htm Wal-Mart, Abbot Labs, US Bancorp, LucasArts, Petrobrás, ...
Government agencies using AI: US National Security Agency
When is a Machine Intelligent?When is a Machine Intelligent? What is Intelligence? What is Intelligence?
Who’s smarter?Who’s smarter? Your medical doctor or
your cleaning lady? Your lawyer or your two
year old daughter? Kasparov or Ronaldinho?
Turing Test
??
1997:2 x 1
2050?2 x 1
What did 40 years of AI research discovered? Common sense
intelligence harder than expert intelligence
Embodied intelligence harder than purely intellectual, abstract intelligence
Kid intelligence harder than adult intelligence
Animal intelligence harder than specifically human intelligence (after all we share 99% of our genes with chimpanzees !)
www.robocup.owww.robocup.orgrg
New benchmark task for AI Annual competition associated to conference on
AI, Robotics or Multi-Agent Systems