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Introduction to Intelligent Agents
Jacques Robin
Ontologies
Reasoning
Components
Agents
Simulations
Outline
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?
Q: What are Software Agents?
A: Software which architecture is based on Artificial Software
the following abstractions: Intelligence Engineering
Immersion in a distributed environment, continuous Agents
thread, encapsulation, sensor, perception, actuator,
action, own goal, autonomous decision making
Q: What is Artificial Intelligence?
Distributed
A: Field of study dedicated to: Systems
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
Artificial 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?
General 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 P
2. Interprets I = f(P)
3. Chooses the next action A = g(I,G) to perform to reach its goal G
4. Use actuator to execute A
What is an Agent?
Agent
P Perception 1. Environment percepts
Sensors Interpretation: 2. Self-percepts
I = f(P) 3. Communicative percepts
Environment
Autonomous
Goals
Reasoning
1. Environment altering
A Action Choice: actions
Effectors 2. Perceptive actions
A = g(I,O)
3. Communicative actions
Agent x Object
Intentionality: Encapsulate own No goal
goals (even if implicitly) in addition
to data and behavior
Decision autonomy:
Pro-actively execute behaviors to No decision autonomy:
satisfy its goals Execute behaviors only reactively
Can negate request for execution whenever invoked by other
of a behavior from another agent objects
More complex input/output: Always execute behavior invoked
percepts and actions by other objects
Temporal continuity: encapsulate Simpler input/output: mere
an endless thread that constantly method parameters and return
monitors the environment values
Coarser granularity:
Temporally discontinuous: active
Encapsulate code of size
comparable to a package or only during the execution of its
component methods
Composed of various objects when
implemented using an OO language
Intelligent Agent x
Simple Software Agent
Percept Interpretation: I = f(P)
Sensors Conventional
AI
Processing
Environment
Goals
Action Choice: A = g(I,O)
Effectors Conventional
AI
Processing
Classical AI
Intelligent Agent
System
Situated Agent Disembodied
AI
Percept System
Interpretation
Sensors
AI Input
Data
Environment
Goals Reasoning
Goal
AI
Action Choice
Effectors Output
AI Data
What is an Agent?
Other Optional Properties
Reasoning Autonomy:
Requires AI, inference engine and knowledge base
Key for: embedded expert systems, intelligent controllers, robots,
games, internet agents ...
Adaptability:
Requires IA, machine learning
Key for: internet agents, intelligent interfaces, ...
Sociability:
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?
Other Optional Properties
Personality:
Requires AI, attitude and emotional modeling
Key for: Digital entertainment, virtual reality avatars,
user-friendly interfaces ...
Temporal continuity and persistence:
Requires interface with operating system, DBMS
Key for: Information filtering, monitoring, intelligent control, ...
Mobility:
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!
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: Abbreviations
4
S B P
A - Agent
G
3 W S, B, G P W - Wumpus
B
P - Pit
G - Gold
2
S B
X? – Possibly X
X! – Confirmed X
V – Visited Cavern
1 A P
B B B – Breeze
start
S – Stench
G – Glitter
1 2 3 4
OK – Safe Cavern
Perceiving, Reasoning and Acting
in the Wumpus World
Percept sequence:
nothing breeze
Wumpus world model maintained by agent:
4 4
3 3
2 ok 2 ok
P?
A
b A
1 1 V
ok P?
ok ok ok
1 2 3 4 1 2 3 4
t=0 t=2
Perceiving, Reasoning and Acting
in the Wumpus World
Percept sequence:
stench {stench, breeze, glitter}
Wumpus World Model:
4 4 P?
3 W! 3 W!
A P?
SBG
2 s A 2 S V V
ok ok ok ok ok
1 V b V P! 1 V b V P!
ok ok ok ok
1 2 3 4 1 2 3 4
Action Sequence:
t=7: Go to (2,1), t=11: Go to (2,3) to find gold
Sole safe unvisited cavern
Classification Dimensions
of 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
Observability
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)
Sources of partial observability:
Realm inaccessible to any available sensor
Limited sensor scope
Limited sensor sensitivity
Noisy sensors
Determinism
Deterministic: all occurrence of executing a given action in a given
situation always yields same result
Non-deterministic (or stochastic): action consequences partially
unpredictable
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: Stationary
and Sequential Environments
Stationary: Single perception-reasoning-action cycle during which
environment is static
Stationary Environment State 1 State 2
Agent
Reasoning
Percept
Ação
Sequential: Sequence of perception-reasoning-action cycle during each of
which the environment changes only as a result of the agent’s actions
Sequential Environment State 1 State 2 State 3 ... State N
Agent
Reasoning Reasoning
Percept
Reasoning
Percept
Percept
Action
Action
Ação
Dynamicity: Concurrent
Synchronous and Asynchronous
Synchronous: Environment can change on its own between one action and the
next perception of an agent, but not during its reasoning
Synchronous Concurrent
Environment State 1 State 2 State 3 State 4 State 5 ...
Agent
Reasoning Reasoning
Percept
Percept
Action
Action
Asynchronous: Environment can change on its own at any time, including
during the agent’s reasoning
Asynchronous Concurrent
Environment State 1 State 2 State 3 State 4 State 5 State 6 ...
Agent
Reasoning Percept
Percept
Action
Action
Reasoning
Multi-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 Variables
MAS variables:
Parameters of agent percepts, actions and goals
Attributes of environment objects
Arguments of environment relations, states, events and locations
Booleanas
Discreta Binárias Dicotômicas
Qualitativas Nominal
Ordinal
Quantitativas Intervalar
Fracional
R
Contínua
[0,1]
Mathematical Domain of Variables
Binary: Interval:
Boolean, ex, Male {True,False} Finite partition of ordered set
Dichotomic, ex, Sex {Male, Female} with measure m defining distance
Nominal (or categorical) d: X,Y, d(X,Y) = |m(X)-m(Y)|
Finite partition of set without order No inherent zero
nor measure ex, Celsius temperature
Relations: only = ou
Fractional (or proportional):
ex, Brazilian, French, British
Partition with distance and
Ordinal (or enumerated): inherent zero
Finite partition of (partially or totally)
ordered set without measure Relations: anyone
Relations: only =, , , > ex, Kelvin temperature
ex, poor, medium, good, excellent Continuous (or real)
Infinite set of values
Other 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 Diversity
Size, i.e., number of instances of: Diversity, i.e., number of classes of:
Agent percepts, actions and goals Agent percepts, actions and goals
Environment agents, objects, Environment agents, objects,
relations, states, events and relations, states, events and
locations locations
Dramatically affects scalability of Dramatically affects scalability of
agent reasoning execution agent knowledge acquisition process
Agents’ 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
Reflex Agent
Sensors
Environment
Rules
Percepts Action
A(t) = h(P(t))
Effectors
Remember …
Agent
P Percept
Sensors Interpretation:
I = f(P)
Environment
Reasoning Goals
A Action Choice:
Effectors
A = g(I,O)
So?
P Percept Interpretation:
Sensors
I = f(P)
Environment
Rules
Percepts Action Goals
A(t) = h(P(t))
A Action Choice:
Effectors
A = g(I,O)
Reflex 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 Agent
Percept Interpretation
Sensors Rules:
percepts(t) model(t) model’(t)
Model Update
Environment
Regras: model(t-1) model(t) (Past and) Current
model’(t) model’’(t) Enviroment Model
Goals
Action Choice
Effectors Rules:
model’’(t) action(t),
action(t) model’’(t) model(t+1)
Automata 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 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 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
Percept Interpretation
Sensors
Rules: percept(t) model(t) model’(t)
Model Update
Rules: model(t-1) model(t) (Past and) Current
model’(t) model’’(t) Environment Model
Environment
Goal Update
Rules: model’’(t) goals(t-1) goals’(t) Goals
Action Choice
Effectors Rules: model’’(t) goals’(t) action(t)
action(t) model’’(t) model(t+1)
(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 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 Y+1 ok
AND (X,Y+1) has not being visited until time T
v
AND (X-1,Y) is safe at time T Y A
AND (X-1,Y) was visited before time T
ok
THEN choose action turn left at time T
X-1 X
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)
Goal-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 Y+1 ok
AND (X,Y+1) has not being visited until time T
v
AND (X-1,Y) is safe at time T Y A
AND (X-1,Y) was visited before time T
ok
THEN choose action forward at time T
X-1 X
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)
Goal-Based Agent Rule Examples
Rule model(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 Agent
Percept Interpretation
Sensors (Past and)
Rules: percept(t) model(t) model’(t)
Current
Environment
Model Update Model
Rules: model(t-1) model(t)
model’(t) model’’(t)
Environment
Goal Update
Rules: model’’(t) goals(t-1) goals’(t)
Goals
Prediction of Future Environments
Rules: model’’(t) model(t+n)
model’’(t) action(t) model(t+1)
Hypothetical
Action Choice Future
Effectors Rules: model(t+n) = result([action1(t),...,actionN(t+n)] Environment
model(t+n) goal(t) do(action(t)) Models
Planning 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)
Hybrid Reflex-Planning Agent
Sensors Reflex Rules Reflex Thread
Percepts Actions
Synchronization
Environment
Planning Thread
Percept Interpretation
Current,
Current Model Update past and
future
Future Environments Prediction environment
Effectors Goal Update model
Action Choice Goals
Hybrid 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 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
Decide Abstractly
Abstract Detail
Perceive in Detail Act in Detail
Layered Automata Agent
Percept Interpretation
Layer2: s(A,B) r(B) q(A)
Layer1: P(s) P(z | y).P(y)
Sensors Layer0: y f(x).dx
Environment Model
Environment Model Update
Layer2: s(A,B) r(B) q(A) Layer2:
Ambiente
s(A,B) r(B) q(A)
Action Choice and Execution Control
Layer2: s(A,B) r(B) q(A)
Layer1: P(s) P(z | y).P(y)
Effectors Layer0: y f(x).dx
Exemplo de camadas de abstração:
Y
X
Abstraction Layer Examples
Y
X
Utility-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 Agent
Percept Interpretation:
Sensors Rules: percept actions
Environment
Goals
Action Choice:
Effectors Utility Function
do( argmax U(a)) u:actions R
a actions
Utility-Based Planning Agent
Sensors Percept Interpretation
Regras: percept(t) model(t) modelo’(t)
Past &
Current
Model Update Environment
Regras: model’(t) model’’(t) Model
Future Environment Prediction
Environment
Regras: model’’(t) ação(t) model(t+1)
model’’(t) model(t+1)
Hypothesized
Future
Utility Function: Environments
u: model(t+n) R Model
Action Choice
Effectors do( argmax U(result([
action1 ... actionn ])))
i i
actioni
1 i
Adaptive Agent
Sensors Performance Analysis Component
Learning Component
Acting
Component • Learn rules or functions:
Environment
• percept(t) action(t)
• Reflex • percept(t) model(t) modelo’(t)
• Automata • modelo(t) modelo’(t)
• Goal-Based • modelo(t-1) modelo(t)
• Planning • modelo(t) action(t)
• action(t) model(t+1)
• Utility-Based
• model(t) goal(t) action(t)
• Hybrid • goal(t) model(t) goal’(t)
• utility(action) = value
• utility(model) = value
Effectors New Problem Generation Component
Simulated 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 environment
2. Send these percepts to the corresponding agents
3. Receives the action chosen by each agent
4. Update the environment to reflect the cumulative consequences of all
these actions
Environment Simulator Architecture
Simulation
Visualization
GUI
Environment Update
Rules: model(t-1) model(t)
action(t) model(t-1) model(t) actions
Agent
Rede Client 1
Simulated Environment
Environment Simulation
Model
...
Server
Agent
percepts Client N
Percept Generation
Rules: model(t) percept(t)
AI’s Pluridisciplinarity
Economics Sociology Zoology Neurology
Psychology
Decision Game (Cognitive)
Paleontology
Theory Theory
Linguistics
Operations
Research
Information
Theory
Mathematics:
• Logic
• Probabilities & Statistics Computer Science:
• Calculus • Theory
• Algebra • Distributed Systems
• Software Engineering
Philosophy • Databases
Artificial
Intelligence
AI Roadmap
Generic Sub-Fields:
• Heuristic Search
• Automated Reasoning & Knowledge Representation Generic Tasks:
• Machine Learning & Knowledge Acquisition • Clustering
Specific Sub-Fields: • Pattern Recognition • Classification
• Multi-Agent Communication, • Temporal Projection
Cooperation & Negotiation • Diagnosis
• Speech & Natural Language Processing • Monitoring
• Computer Perception & Vision • Repair
• Robotic Navigation & Manipulation • Control
• Games • Recommendation
• Intelligent Tutoring Systems Computational Metaphors: • Configuration
• Algorithmic Exploration • Discovery
• Logical Derivation • Design
• Probability Estimation • Allocation
• Connectionist Activation • Timetabling
• Evolutionary Selection • Planning
• Simulation
AI Metaphors, Abstractions
Problem Algorithm
+ P(A|B)
Today’s Diversity of AI Applications
Agriculture, Natural Resource Law
Management, and the Environment Law Enforcement & Public Safety
Architecture & Design Libraries
Art Marketing, Customer Relations & E-
Artificial Noses Commerce
Astronomy & Space Exploration Medicine
Assistive Technologies Military
Banking, Finance & Investing Music
Bioinformatics Networks - including Maintenance,
Business & Manufacturing Security & Intrusion Detection
Drama, Fiction, Poetry, Storytelling & Politics & Foreign Relations
Machine Writing Public Health & Welfare
Earth & Atmospheric Sciences Scientific Discovery
Engineering Social Science
Filtering Sports
Fraud Detection & Prevention Telecommunications
Hazards & Disasters Transportation & Shipping
Information Retrieval & Extraction Video Games, Toys. Robotic Pets &
Knowledge Management Entertainment
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?
What is Intelligence?
Turing Test
Who’s smarter?
Your medical doctor or your
cleaning lady? ?
Your lawyer or your two
year old daughter?
Kasparov or Ronaldo?
What did 40 years of AI
research discovered?
Common sense intelligence
1997:
harder than expert
intelligence
Embodied intelligence
harder than purely
2x1
intellectual, abstract
intelligence
Kid intelligence harder
than adult intelligence
Animal intelligence harder
2050?
than specifically human
2x1
intelligence (after all we
share 99% of our genes
with chimpanzees !)
www.robocup.org
New benchmark task for AI
Annual competition associated to conference
on AI, Robotics or Multi-Agent Systems
Tomorrow’s AI
Applications Blade Runner
M
A
T
R
I
X
A.I.
