Part I, Chapter 1 – Four Basic Topics AI and computer science have already set about trying to...
-
Upload
emory-reynolds -
Category
Documents
-
view
215 -
download
1
Transcript of Part I, Chapter 1 – Four Basic Topics AI and computer science have already set about trying to...
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)
Insert picture here
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