Part I, Chapter 1 – Four Basic Topics

AI and computer science have already setabout trying to fill … niches, and that is a worthy, if never-ending, pursuit. But the biggestprize, I think, is for the creation of an artificial intelligence as flexible as the biological onesthat will win it. Ignore the naysayers; go for it!

Nils J. Nilsson The Eye on the Prize

AI Magazine (1995)

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Chapter IChapter IFour Basic Topics Four Basic Topics

in AIin AI

Part I, Chapter 1 – Four Basic Topics

ContentContent

Chapter 1 - Four Basic Topics:

1.1 Cooperation: Intelligent Agents

1.2 Representation

1.3 Search

1.4 Learning

PI – 1.1 Intelligent Agents

1.1.1 Agent Architecture

1.1.2 Multiagent Systems

Chapter I – Four Basic Topics in AIChapter I – Four Basic Topics in AI

1.1 COOPERATION: Intelligent Agents

PI – 1.1 Intelligent Agents

1.1.1 Agent Architecture1.1.1 Agent Architecture

PI – 1.1 Intelligent Agents

Definition: What is an Agent?Definition: What is an Agent?

- Rao, Georgeff (91); Russell, Norvig (95)

- Wooldrige, Jennings (95):

- Autonomy - Reactivity - Pro-activity - Social Ability: Communication and Social Organization

PI – 1.1 Intelligent Agents

Properties of Agents (Jennings/Wooldrige)Properties of Agents (Jennings/Wooldrige)

Weak Notion Stronger Notion Other of Agency of Agency Properties

Autonomy Knowledge/Belief Rational

Social Ability Intentions Truthful ?

Reactivity Desires/Goals Benevolent

Pro-Activeness Obligations Mobile

Emotions

Weak Notion Stronger Notion Other of Agency of Agency Properties

Autonomy Knowledge/Belief Rational

Social Ability Intentions Truthful ?

Reactivity Desires/Goals Benevolent

Pro-Activeness Obligations Mobile

Emotions

PI – 1.1 Intelligent Agents

The Agent Architecture: A ModelThe Agent Architecture: A ModelExtension Basic Model

Actuators

Motorics

SensorsMouth: Communication

DeviceHead: General Abilities

Body: Application- specific Abilities

RobotsSoftbotsMobile AgentsPDAs etc

PI – 1.1 Intelligent Agents

AIMA codeAIMA code

The code for each topic is devided into four directories:- agents: code defining agent types and programs

- algorithms: code for the methods used by the agent programs

- environments: code defining environment types, simulations

- domains: problem types and instances for input to algorithms

PI – 1.1 Intelligent Agents

AIMA code - ExampleAIMA code - Example

(setq joe (make-agent:name joe:body (make agent-body):program (make-dumb-agent-program)))

(defun make-dumb-agent-program ()(let ((memory nil))#’(lambda (percept)

(push percept memory)’no-op)))

PI – 1.1 Intelligent Agents

Skeleton of an agent Skeleton of an agent

function SKELETON-AGENT (percept) returns action

static: memory, the agent‘s memory of the

world

memory ← UPDATE-MEMORY (memory, percept)

action ← CHOOSE-BEST-ACTION (memory)

memory ← UPDATE-MEMORY (memory, action)

return action

PI – 1.1 Intelligent Agents

TYPE 1:Simple Reflex AgentsTYPE 1:Simple Reflex Agents

PI – 1.1 Intelligent Agents

Schema of a simple reflex agentSchema of a simple reflex agent

function SIMPLE-REFLEX-AGENT (percept)returns actionstatic: rules, a set of condition action rulesstate ← INTERPRET-INPUT (percept)rule ← RULE-MATCH (state, rules)action ← RULE-ACTION (rule)return action

PI – 1.1 Intelligent Agents

TYPE 2: State-based AgentsTYPE 2: State-based Agents

PI – 1.1 Intelligent Agents

Schema of a Reflex Agent with StateSchema of a Reflex Agent with State (state (state ≈ ≈ internal representation of the world)internal representation of the world)

function REFLEX-AGENT-WITH-STATE (percept)returns actionstatic: rules, a set of condition action rulesstate ← UPDATE-STATE (state, percept)rule ← RULE-MATCH (state, rules)action ← RULE-ACTION [rule]state ← UPDATE-STATE (state, action)return action

PI – 1.1 Intelligent Agents

TYPE 3: Goal-based AgentsTYPE 3: Goal-based Agents

PI – 1.1 Intelligent Agents

TYPE 4: Learning Agents/Utility based AgentsTYPE 4: Learning Agents/Utility based Agents

PI – 1.1 Intelligent Agents

Classification of AgentsClassification of Agents

Utility-based Agents/Learning Agents:Type 4

Nilsson, Russel & Norvig

Goal-based Agents: Type 3

State-based Agents:Type 2

Simple Reflex Agents: Type1

TYPE 5:Consciousness

?

PI – 1.1 Intelligent Agents

The Agent Architecture InteRRaPThe Agent Architecture InteRRaP

E N V I R O N M E N T

Ag

ent

Co

ntr

ol U

nit

Hie

rach

ical

Ag

en

t K

B

A ctin g C om m unication Perception

Kn

ow

led

ge

Ab

stra

ction

w o r l d i n t e r f a c e ( W I F )

World Model (situational context)

Patterns of Behaviour

Cooperation Knowledge (social context)

Joint Goals / Plans

Planning Knowledge (m ental context)

Local Goals / Plans

control flow information access

Cooperative PlanningLayer (CPL)

Local Planning Layer(LPL)

Behaviour-BasedLayer (BBL)

PI – 1.1 Intelligent Agents

Intuitive View of the InteRRaP Agent ArchitectureIntuitive View of the InteRRaP Agent Architecture

CooperativePlanning Layer

Local Planning Layer

Behaviour-BasedLayer

not pnot f

fp

fp

not pnot f

acceptpropose

reject

Part I, Chapter 1 – Four Basic Topics

DISTRIBUTED ARTIFICIAL INTELLIGENCE :DISTRIBUTED ARTIFICIAL INTELLIGENCE :DAI integrates many AI topicsDAI integrates many AI topics

PI – 1.1 Intelligent Agents

1.1.2 Multiagent Systems1.1.2 Multiagent Systems

Cooperation

PI – 1.1 Intelligent Agents

Shift of Programming ParadigmShift of Programming Paradigm

divide and conqueremergent problem solving behaviour

task

devide

local problemsolving &interaction

integration

solution

PI – 1.1 Intelligent Agents

Natural MAS: Ants have astonishing AbilitiesNatural MAS: Ants have astonishing Abilities

PI – 1.1 Intelligent Agents

PI – 1.1 Intelligent Agents

PI – 1.1 Intelligent Agents

Ant Attack: DescriptionAnt Attack: Description

Plate 15. The red Amazon ants (Polyergus rufescens) invade the nest of Formica fusca to capture the pupae. At this moment, the scouts that discovered the site are leading a raiding party into the nest interior. Some defenders grasp the brood and attempt to flee. The mandibles of Polyergus are specialized fighting weapons with which the can easily penetrate the Formica worker’s cuticle. (From Hölldobler, 1984d; painting by J. D. Dawson reprinted with permission of the National Geographic Society.)

PI – 1.1 Intelligent Agents

PI – 1.1 Intelligent Agents

Weaver ant: DescriptionWeaver ant: Description

Plate 6. The African weaver ant, Oecophylla longinoda, establishes large territories in tree canopies. The maintenance and defense of the territories are organizes by a complex communication system. Confronting a stranger (left foreground), a worker displays hostility with gaping mandibles and the gaster cocked over the forward part of the body. Another pair in the background are clinched in combat. Rushing toward the leaf nest (upper right), another ant lays an odor trail with secretions from the rectal gland at the abdominal tip. The chemical substances in this trail will lead reinforcements to the fray. When capturing a prey object, such as a giant black African stink ant (Paltothyreus tarsatus), ant organize cooperation by means of chemical short-range recruitment signals from the sternal gland and alarm pheromones from the mandibular gland. (From Hölldobler, 1984d; painting by J. D. Dawson reprinted with permission of the National Geographic Society.)

PI – 1.1 Intelligent Agents

… yet, the brain is only a small finite state machine!

PI – 1.1 Intelligent Agents

MAS-Research at DFKI in SaarbrückenMAS-Research at DFKI in Saarbrücken

DFKI

PI – 1.1 Intelligent Agents

DFKI: Autonomous Cooperating AgentsDFKI: Autonomous Cooperating AgentsCommonsense Reasoning

Intelligent Expert

Cooperation

Agent 1 Agent 2 Agent 3

Technical Applications:• Interacting Robots• Air Traffic Control Systems• Scheduling and Planning in CIM and Logistics• Storehouse Administration• Games • etc.

SOFTBOTS ROBOTS

Cooperation

PI – 1.1 Intelligent Agents

DFKI: Physical Implementation of the Loading DockDFKI: Physical Implementation of the Loading Dock

PI – 1.1 Intelligent Agents

DFKI: Implementation in a 3D Simulated WorldDFKI: Implementation in a 3D Simulated World

PI – 1.1 Intelligent Agents

Traffic Telematics is one of the Main Traffic Telematics is one of the Main Application AreasApplication Areas

Transportation Agency 1

Truck Agents

CompanyAgent 1

Transportation Agency 2

Truck Agents

CompanyAgent 2

PI – 1.1 Intelligent Agents

DFKI: The Project TELETRUCKDFKI: The Project TELETRUCK

Shipping Company

Truck Agents

CompanyAgent

GPSInmarsat - CModoacomC-NetD-Net

VehicleTracking

ShippingCompany

Optimi-sation

Intergration

SQLDB

ChipCard

CardReader

Datex-P

PI – 1.1 Intelligent Agents

TTELEELETTRUCK: RUCK: Resources and AllocationResources and Allocation

PnEU

Trailer

Driver

Truck

PnEU

Truck

Driver

Driver

PnEU

Truck

Trailer

Resource Allocation Protocol

Extended Contract Net Protocol

Company resources:

• Delivery tasks• Planning and execution time• Repair capacities• Fleet size• Freight monopolies• Geographical dispersion

Inner-agent resources:

• Fuel• Load capacity• Repair state• State of the human dirver

PI – 1.1 Intelligent Agents

Task Allocation in the Transportation DomainTask Allocation in the Transportation Domain

Select Bidders

Select Best Bid

Evaluate Orders

Execute Task

Announce Task

Bid for Task

Award Task

VER

TIC

AL C

OO

PER

ATIO

NTHE CONTRACT NET:

PI – 1.1 Intelligent Agents

PI – 1.1 Intelligent Agents

ROBO CUP: ExamplesROBO CUP: Examples

• Ressources:– stamina– attack– defence

• Emotional States:– fear: → attack

→ flight & run– hunger: → appetence SFB-387:

“Resource Limited Cognitive Processes”

PI – 1.1 Intelligent Agents

Future of MAS

Emotions and Resources 

Emotions are part of a management system to co-ordinate each individual’s multiple plans and goals under constraints of time and other resources. Emotions are part of the biological solution to the problem of how to plan and to carry out action aimed at satisfying multiple goals in environments which ate mot perfectly predictable.  – Oatley and Johnson-Laird

PI – 1.1 Intelligent Agents

Resource Driven Concurrent ComputationResource Driven Concurrent Computation

Process Space

ComputationalThread

Resources

Meta-Control