Introduction to intelligent agents
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Introduction to Intelligent Agents. Jacques Robin. 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. Software

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Introduction to intelligent agents

Introduction to Intelligent Agents

Jacques Robin


Outline

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

Software

Engineering

Artificial

Intelligence

Agents

Distributed

Systems

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 characteristics

    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

    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


    What is an agent

    Agent

    Perception

    Interpretation: I = f(P)

    P

    Goals

    Action Choice:A = g(I,O)

    A

    What is an Agent?

    Environment

    • Environment percepts

    • Self-percepts

    • Communicative percepts

    Sensors

    Autonomous

    Reasoning

    • Environment altering actions

    • Perceptive actions

    • Communicative actions

    Effectors


    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

    Agent x Object


    Intelligent agent x simple software agent

    Environment

    Percept Interpretation: I = f(P)

    AI

    Conventional

    Processing

    Conventional

    Processing

    Sensors

    Goals

    Action Choice: A = g(I,O)

    Effectors

    AI

    Intelligent Agent x Simple Software Agent


    Intelligent agent

    Disembodied

    AI

    System

    Situated Agent

    Environment

    Percept

    Interpretation

    Sensors

    InputData

    AI

    Reasoning

    Goals

    Goal

    AI

    Action Choice

    Effectors

    Output

    Data

    AI

    Intelligent Agent

    Classical AI System


    What is an agent other optional properties

    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 properties1

    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

    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

    P

    B

    S

    G

    W

    P

    3

    S, B, G

    B

    2

    S

    B

    A

    1

    P

    B

    B

    start

    1

    2

    3

    4

    Wumpus World: Abbreviations

    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


    Perceiving reasoning and acting in the wumpus world

    4

    4

    3

    3

    2

    2

    ok

    A

    P?

    V

    A

    ok

    1

    1

    ok

    ok

    P?

    b

    ok

    1

    2

    3

    4

    1

    2

    3

    4

    ok

    Perceiving, Reasoning and Actingin the Wumpus World

    • Percept sequence:

    nothing

    breeze

    • Wumpus world model maintained by agent:

    t=0

    t=2


    Perceiving reasoning and acting in the wumpus world1

    4

    P?

    4

    A

    P?

    W!

    3

    S B G

    W!

    3

    2

    V

    V

    S

    ok

    ok

    ok

    A

    s

    2

    ok

    ok

    V

    V

    P!

    1

    b

    ok

    ok

    V

    V

    P!

    1

    b

    ok

    ok

    1

    2

    3

    4

    1

    2

    3

    4

    Perceiving, Reasoning and Actingin the Wumpus World

    • Percept sequence:

    stench

    {stench, breeze, glitter}

    • Wumpus World Model:

    • Action Sequence:

    t=11: Go to (2,3) to find gold

    t=7: Go to (2,1), Sole safe unvisited cavern


    Classification dimensions of agent environments

    Classification Dimensionsof 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

    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

    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

    Dynamicity: Stationaryand Sequential Environments

    • Stationary: Single perception-reasoning-action cycle during which environment is static

    • Sequential: Sequence of perception-reasoning-action cycle during each of which the environment changes only as a result of the agent’s actions

    State 1

    State 2

    Stationary Environment

    Agent

    Reasoning

    Percept

    Ação

    ...

    Sequential Environment

    State N

    State 1

    State 2

    State 3

    Agent

    Reasoning

    Reasoning

    Reasoning

    Percept

    Action

    Percept

    Action

    Percept

    Ação


    Dynamicity concurrent synchronous and asynchronous

    ...

    Synchronous Concurrent

    Environment

    State 2

    State 3

    State 1

    State 4

    State 5

    Agent

    Reasoning

    Reasoning

    Percept

    Action

    Percept

    Action

    Asynchronous Concurrent

    Environment

    ...

    State 2

    State 3

    State 6

    State 1

    State 4

    State 5

    Agent

    Reasoning

    Percept

    Action

    Percept

    Action

    Reasoning

    Dynamicity: ConcurrentSynchronous 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


    Multi agency

    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

    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

    Intervalar

    Quantitativas

    Fracional

    R

    Contínua

    [0,1]


    Mathematical domain of variables1

    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

    Mathematical Domain of Variables


    Other characteristics

    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:

    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

    Size and Diversity


    Agents internal architectures

    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

    Reflex Agent

    Environment

    Sensors

    Rules

    Percepts  Action

    A(t) = h(P(t))

    Effectors


    Remember

    Agent

    P

    Reasoning

    Action Choice:A = g(I,O)

    A

    Remember …

    Environment

    Percept

    Interpretation: I = f(P)

    Sensors

    Goals

    Effectors


    Introduction to intelligent agents

    Environment

    Percept Interpretation: I = f(P)

    P

    Sensors

    Rules

    Percepts  Action

    A(t) = h(P(t))

    Goals

    A

    Action Choice:A = g(I,O)

    Effectors

    So?


    Reflex agent1

    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

    Automata Agent

    Environment

    Percept Interpretation

    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


    Automata agent1

    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

    Automata Agent Rule Examples

    • Rules percept(t) model(t)  model’(t)

      • IF visualPercept at time T is glitterAND 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 TAND 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 examples1

    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

    (Explicit) Goal-Based Agent

    Environment

    Percept Interpretation

    Rules: percept(t)  model(t)  model’(t)

    Sensors

    (Past and) CurrentEnvironment 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


    Explicit goal based agent1

    (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

    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)


    Goal based agent rule examples1

    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)


    Goal based agent rule examples2

    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

    Planning Agent

    Environment

    (Past and)Current

    Environment

    Model

    Percept Interpretation

    Rules: percept(t)  model(t)  model’(t)

    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


    Planning agent1

    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

    Synchronization

    Hybrid Reflex-Planning Agent

    Environment

    Reflex Thread

    Reflex Rules

    Percepts Actions

    Sensors

    Planning Thread

    Current,

    past and

    future

    environment

    model

    Percept Interpretation

    Current Model Update

    Future Environments Prediction

    Effectors

    Goal Update

    Goals

    Action Choice


    Hybrid reflex planning agent1

    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

    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

    Ambiente

    Percept Interpretation

    Layer2:

    Layer1:

    Sensors

    Layer0:

    Environment Model

    Environment Model Update

    Layer2:

    Layer2:

    Action Choice and Execution Control

    Layer2:

    Layer1:

    Effectors

    Layer0:

    Layered Automata Agent


    Exemplo de camadas de abstra o

    Y

    X

    Exemplo de camadas de abstração:


    Abstraction layer examples

    Y

    X

    Abstraction Layer Examples


    Utility based agent

    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

    Environment

    Percept Interpretation:

    Rules: percept  actions

    Sensors

    Goals

    Effectors

    Utility-Based Reflex Agent

    Action Choice:

    Utility Function

    u:actions  R


    Utility based planning agent

    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


    Adaptive agent

    Performance Analysis Component

    Learning Component

    Adaptive Agent

    Environment

    Sensors

    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

    New Problem Generation Component

    Effectors


    Simulated environments

    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


    Environment simulator architecture

    actions

    Agent

    Client 1

    Agent

    Client N

    percepts

    Environment Simulator Architecture

    Simulation

    Visualization

    GUI

    Rede

    Environment Update

    Rules: model(t-1)  model(t)

    action(t)  model(t-1)  model(t)

    Simulated

    Environment

    Model

    Environment

    Simulation

    Server

    ...

    Percept Generation

    Rules: model(t) percept(t)


    Ai s pluridisciplinarity

    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


    Ai roadmap

    • Generic Sub-Fields:

    • Heuristic Search

    • Automated Reasoning & Knowledge Representation

    • Machine Learning & Knowledge Acquisition

    • Pattern Recognition

    • 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

    AI Metaphors, Abstractions

    Problem

    Algorithm

    + P(A|B)

    AI Roadmap

    • Generic Tasks:

    • Clustering

    • Classification

    • Temporal Projection

    • Diagnosis

    • Monitoring

    • Repair

    • Control

    • Recommendation

    • Configuration

    • Discovery

    • Design

    • Allocation

    • Timetabling

    • Planning

    • Simulation


    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

    Today’s Diversity of AI Applications


    Examples of ai applied to banking finance and investment

    Examples of AI Applied to Banking, Finance and Investment

    • Stock market (or currency) value prediction

      • From set of publicly release data about company (or economy)

      • From past market fluctuation of the company’s shares (or exchange rates)

      • From comparison with similar or concurrent stocks (or currencies)

      • From multi-agent trading simulations

    • Trading software agents

      • Beat human traders in a commodity trading contest in 2001 (BBC)

      • 33% of electronic trading is AI-assisted (Financial Post, 2004)

    • Loan approval

    • Fraud detection

      • Mining suspicious patterns in transaction logs

      • Credit card fraud

      • Insider trading

      • Money laundering

    • Financial news filtering and summarization


    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 what is intelligence

    Turing Test

    1997:

    2 x 1

    ?

    2050?

    2 x 1

    When is a Machine Intelligent? What is Intelligence?

    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 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 org

    www.robocup.org

    • New benchmark task for AI

    • Annual competition associated to conference on AI, Robotics or Multi-Agent Systems


    Tomorrow s ai applications

    BladeRunner

    M

    A

    T

    R

    I

    X

    A.I.

    Tomorrow’s AI Applications


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