1. Introduction to Intelligent Agents
Jacques Robin

2. 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

3. 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
Artificial
Intelligence
Agents
Distributed
Systems
Software
Engineering

4. 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)

5. 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:
Use sensor to perceive P
Interprets I = f(P)
Chooses the next action A = g(I,G) to perform to reach its goal G
Use actuator to execute A

6. What is an Agent? Agent Environment Perception
Interpretation: I = f(P) P
Environment percepts
Self-percepts
Communicative percepts
Sensors
Goals
Action Choice: A = g(I,O)
Autonomous
Reasoning
Environment altering actions
Perceptive actions
Communicative actions A
Effectors

7. Agent 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

8. Intelligent Agent x Simple Software Agent
Environment
Sensors
Effectors
Goals
Percept Interpretation: I = f(P)
Action Choice: A = g(I,O)
Conventional
Processing AI

9. Classical AI System Intelligent Agent Situated Agent Disembodied AI
Environment
Sensors
Effectors
Goals
Percept
Interpretation
Action Choice AI
Situated Agent
Reasoning
Input Data
Output
Data
Goal
Disembodied AI
System

10. 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, ...

11. 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

12. 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

13. Wumpus World: Abbreviations1 2 3 4
start S A B P W
S, B, G G
A - Agent
W - Wumpus
P - Pit
G - Gold
X? – Possibly X
X! – Confirmed X
V – Visited Cavern
B – Breeze
S – Stench
G – Glitter
OK – Safe Cavern

14. Perceiving, Reasoning and Acting in the Wumpus World
Percept sequence:
nothing
breeze
Wumpus world model maintained by agent: 1 2 3 4 A ok 1 2 3 4 ok A V P? b
t=0
t=2

15. Perceiving, Reasoning and Acting in the Wumpus World
Percept sequence:
stench
{stench, breeze, glitter}
Wumpus World Model: 1 2 3 4 ok A V b W! s P! 1 2 3 4 ok A S V b P! W!
S B G P?
Action Sequence:
t=11: Go to (2,3) to find gold
t=7: Go to (2,1), Sole safe unvisited cavern

16. 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

17. 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

18. 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

19. Dynamicity: Static 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
Static Environment
State 1
State 2
Agent
Reasoning
Percept
Ação
...
Sequential Environment
State 1
State 2
State 3
State N
Agent
Reasoning
Reasoning
Reasoning
Percept
Action
Percept
Action
Percept
Ação

20. 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
Asynchronous: Environment can change on its own at any time, including during the agent’s reasoning
...
Percept
Synchronous Concurrent
Environment
Agent
Action
State 1
Reasoning
State 2
State 4
State 5
State 3
...
Percept
Asynchronous Concurrent
Environment
Agent
Action
State 1
Reasoning
State 2
State 4
State 3
State 5
State 6

21. Dynamicity: Stationary and Non-Stationary
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

22. Multi-Agency Sophistication of agent society: Main classes:
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

23. 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
Boolean
Discrete
Binary
Dichotomical
Qualitative
Nominal
Ordinal
Interval
Quantitative
Fractional R
Continuous
[0,1]

24. Mathematical Domain of Variables
Binary:
Boolean, ex, Male  {True,False}
Dichotomic, ex, Sex  {Male, Female}
Nominal (or categorical)
Finite partition of set without order nor measure
Relations: only = ou 
ex, Brazilian, French, British
Ordinal (or enumerated):
Finite partition of (partially or totally) ordered set without measure
Relations: only =, , , >
ex, poor, medium, good, excellent
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):
Partition with distance and inherent zero
Relations: anyone
ex, Kelvin temperature
Continuous (or real)
Infinite set of values

25. 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

26. Size and Diversity Size, 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, 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

27. 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

28. Reflex Agent Environment Sensors Rules Percepts  Action
A(t) = h(P(t))
Effectors

29. Remember … Agent Environment Percept Interpretation: I = f(P) P
Reasoning
Percept
Interpretation: I = f(P) P
Sensors
Action Choice: A = g(I,O)
Goals A
Effectors

30. So? Environment P Percept Interpretation: I = f(P) Sensors Rules
Effectors
Rules
Percepts  Action
A(t) = h(P(t)) A P
Goals
Percept Interpretation: I = f(P)
Action Choice: A = g(I,O)

31. Reflex Agent Principle: Wumpus World example Pros: Cons:
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.

32. Automata Agent Goals Percept Interpretation Environment Model Update
Rules:
percepts(t)  model(t)  model’(t)
Sensors
(Past and) Current
Enviroment Model
Model Update
Regras: model(t-1)  model(t)
model’(t)  model’’(t)
Goals
Action Choice
Rules:
model’’(t)  action(t),
action(t)  model’’(t)  model(t+1)
Effectors

33. 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)

34. 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).

35. 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).

36. (Explicit) Goal-Based Agent
Environment
Percept Interpretation
Rules: percept(t)  model(t)  model’(t)
Sensors
(Past and) Current Environment Model
Model Update
Rules: model(t-1)  model(t)
model’(t)  model’’(t)
Goal Update
Rules: model’’(t)  goals(t-1)  goals’(t)
Goals
Action Choice
Rules: model’’(t)  goals’(t)  action(t) action(t)  model’’(t)  model(t+1)
Effectors

37. (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

38. 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 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 Y
v ok A
X-1 X

39. 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 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 Y
v ok A
X-1 X

40. 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))).

41. Planning Agent Environment Percept Interpretation Sensors
Rules: percept(t)  model(t)  model’(t)
(Past and) Current
Environment
Model
Sensors
Model Update
Rules: model(t-1)  model(t)
model’(t)  model’’(t)
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
Future
Environment
Models
Action Choice
Rules: model(t+n) = result([action1(t),...,actionN(t+n)]
 model(t+n)  goal(t)  do(action1(t))
Effectors

42. 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)

43. Hybrid Reflex-Planning Agent
Environment
Reflex Thread
Reflex Rules
Percepts  Actions
Sensors
Synchronization
Planning Thread
Percept Interpretation
Current,
past and
future
environment
model
Current Model Update
Future Environments Prediction
Effectors
Goal Update
Goals
Action Choice

44. 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

45. 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

46. Layered Automata Agent
Percept Interpretation
Ambiente
Sensors
Effectors
Environment Model
Environment Model Update
Action Choice and Execution Control
Layer2:
Layer2:
Layer1:
Layer0:
Layer2:
Layer1:
Layer0:

47. Exemplo de camadas de abstração:Y

48. Abstraction Layer Examples

49. 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

50. Utility-Based Reflex Agent
Environment
Sensors
Effectors
Percept Interpretation:
Rules: percept  actions
Goals
Action Choice:
Utility Function
u:actions  R

51. Utility-Based Planning Agent
Environment
Percept Interpretation
Regras: percept(t)  model(t)  modelo’(t)
Sensors
Past &
Current
Environment
Model
Model Update
Regras: model’(t)  model’’(t)
Future Environment Prediction
Regras: model’’(t)  ação(t)  model(t+1)
model’’(t)  model(t+1)
Hypothesized
Future
Environments
Model
Utility Function: u: model(t+n)  R
Action Choice
Effectors

52. Adaptive Agent Performance Analysis Component Environment
Sensors
Learning Component
Performance Analysis Component
Acting
Component
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
Reflex
Automata
Goal-Based
Planning
Utility-Based
Hybrid
Effectors
New Problem Generation Component

53. 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:
Compute percept of each agent in current environment
Send these percepts to the corresponding agents
Receives the action chosen by each agent
Update the environment to reflect the cumulative consequences of all these actions

54. Environment Simulator Architecture
Simulation
Visualization
GUI
Rede
Environment Update
Rules: model(t-1)  model(t)
action(t)  model(t-1)  model(t)
actions
Agent
Client 1
Simulated
Environment
Model
percepts
Environment
Simulation
Server
...
Agent
Client N
Percept Generation
Rules: model(t)  percept(t)

55. AI’s Pluridisciplinarity
Economics
Sociology
Zoology
Neurology
Psychology
(Cognitive)
Decision
Theory
Game
Theory
Paleontology
Linguistics
Operations
Research
Information
Theory
Mathematics:
Logic
Probabilities & Statistics
Calculus
Algebra
Computer Science:
Theory
Distributed Systems
Software Engineering
Databases
Philosophy
Artificial
Intelligence

56. AI Metaphors, Abstractions
AI Roadmap
Generic Sub-Fields:
Heuristic Search
Automated Reasoning & Knowledge Representation
Machine Learning & Knowledge Acquisition
Pattern Recognition
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
Computational Metaphors:
Algorithmic Exploration
Logical Derivation
Probability Estimation
Connectionist Activation
Evolutionary Selection
Problem
Algorithm
+ P(A|B)
AI Metaphors, Abstractions

57. Today’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

58. AI Pays ! AI Industry Gross Revenue: Companies specialized in AI:
2002: US $11.9 billions
Annual growth rate: 12.2%
Projection for 2007: $21.2 billions
Companies specialized in AI:
Corporations developing and using AI:
Google, Amazon, IBM, Microsoft, Yahoo, ...
Corporations using IA:
Wal-Mart, Abbot Labs, US Bancorp, LucasArts, Petrobrás, ...
Government agencies using AI:
US National Security Agency

59. 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 Ronaldinho?
1997:
2 x 1
2050?
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 !)

60. www.robocup.org New benchmark task for AI
Annual competition associated to conference on AI, Robotics or Multi-Agent Systems

61. Tomorrow’s AI ApplicationsX
Blade Runner
A.I.