SlideShare a Scribd company logo
Architecture for intelligent
agents ,Agent communication
Unit 3
DEFINITION
• Agent architectures, like software architectures, are formally a description of the
• elements from which a system is built and the manner in which they communicate. Further,
• these elements can be defined from patterns with specific constraints. [Shaw/Garlin 1996]
• 1. A number of common architectures exist that go by the names pipe-and filter or layered
architecture.
• 2. these define the interconnections between components.
• 3. Pipe-and-Filter defines a model where data is moved through a set of one or more objects
that perform a transformation.
• 4. Layered simply means that the system is comprised of a set of layers that provide a
specific set of logical functionality and that connectivity is commonly restricted to the layers
contiguous to one another.
TYPES OF ARCHITECTURES
• Based on the goals of the agent application, a variety of agent architectures exist to
• help. This section will introduce some of the major architecture types and applications for
• which they can be used.
1. Reactive architectures
2. Deliberative architectures
3. Blackboard architectures
4. Belief-desire-intention (BDI) architecture
5. Hybrid architectures
6. Mobile architectures
REACTIVE ARCHITECTURES
1. A reactive architecture is the simplest architecture for
agents.
2. In this architecture, agent behaviors are simply a mapping
between stimulus and response.
3. The agent has no decision-making skills, only reactions to
the environment in which it exists.
4. The agent simply reads the environment and then maps
the state of the environment to one or more actions. Given
the environment, more than one action may be appropriate,
and therefore the agent must choose.
5. The advantage of reactive architectures is that they are
extremely fast.
6. This kind of architecture can be implemented easily in
hardware, or fast in software lookup.
7. The disadvantage of reactive architectures is that they
apply only to simpleenvironments.
8. Sequences of actions require the presence of state, which
is not encoded into the mapping function.
DELIBERATIVE ARCHITECTURES
1. A deliberative architecture, as the name implies, is one that includes
some deliberation over the action to perform given the current set of
inputs.
2. Instead of mapping the sensors directly to the actuators, the
deliberative architecture considers the sensors, state, prior results of
given actions, and other information in order to select the best action to
perform.
3. The mechanism for action selection as is undefined. This is because it
could be a variety of mechanisms including a production system, neural
network, or any other intelligent algorithm.
4. The advantage of the deliberative architecture is that it can be used
to solve much more complex problems than the reactive architecture.
5. It can perform planning, and perform sequences of actions to achieve
a goal.
6. The disadvantage is that it is slower than the reactive architecture
due to the deliberation for the action to select.
BLACKBOARD ARCHITECTURES
1. The blackboard architecture is a very common architecture that is also very interesting.
2. The first blackboard architecture was HEARSAY-II, which was a speech understanding
system. This architecture operates around a global work area call the blackboard.
3. The blackboard is a common work area for a number of agents that work cooperatively
to solve a given problem.
4. The blackboard therefore contains information about the environment, but also
intermediate work results by the cooperative agents.
5. In this example, two separate agents are used to sample the environment through the
available sensors (the sensor agent) and also through the available actuators (action
agent).
6. The blackboard contains the current state of the environment that is constantly
updated by the sensor agent, and when an action can be performed (as specified in the
blackboard), the action agent translates this action into control of the actuators.
Contd....
7. The control of the agent system is provided by one or more reasoning agents.
8. These agents work together to achieve the goals, which would also be
contained in the blackboard.
9. In this example, the first reasoning agent could implement the goal definition
behaviors, where the second reasoning agent could implement the planning
portion (to translate goals into sequences of actions).
10. Since the blackboard is a common work area, coordination must be
provided such that agents don’t step over one another.
11. For this reason, agents are scheduled based on their need. For example,
agents canbmonitor the blackboard, and as information is added, they can
request the ability toboperate.
12. The scheduler can then identify which agents desire to operate on the
blackboard, and then invoke them accordingly.
13. The blackboard architecture, with its globally available work area, is easily
implemented with a multi-threading system.
14. Each agent becomes one or more system threads. From this perspective, the
blackboard architecture is very common for agent and non-agent systems.
BELIEF-DESIRE-INTENTION (BDI) ARCHITECTURE
1. BDI, which stands for Belief-Desire-Intention, is an architecture that
follows the theory of human reasoning as defined by Michael Bratman.
2. Belief represents the view of the world by the agent (what it believes
to be the state of the environment in which it exists). Desires are the
goals that define the motivation of the agent (what it wants to achieve).
3. The agent may have numerous desires, which must be consistent.
Finally, Intentions specify that the agent uses the Beliefs and Desires in
order to choose one or more actions in order to meet the desires.
4. As we described above, the BDI architecture defines the basic
architecture of any deliberative agent. It stores a representation of the
state of the environment (beliefs), maintains a set of goals (desires), and
finally, an intentional element that maps desires to beliefs (to provide
one or more actions that modify the state of the environment based on
the agent’s needs).
HYBRID ARCHITECTURES
1. As is the case in traditional software architecture, most architectures are
hybrids.
2. For example, the architecture of a network stack is made up of a pipe-and-
filter architecture and a layered architecture.
3. This same stack also shares some elements of a blackboard architecture, as
there are global elements that are visible and used by each component of the
architecture.
4. The same is true for agent architectures. Based on the needs of the agent
system,different architectural elements can be chosen to meet those needs.
MOBILE ARCHITECTURES
1. This architectural pattern introduces the ability for agents to migrate themselves
between hosts. The agent architecture includes the mobility element, which allows an
agent to migrate from one host to another.
3. An agent can migrate to any host that implements the mobile framework.
4. The mobile agent framework provides a protocol that permits communication between
hosts for agent migration.
5. This framework also requires some kind of authentication and security, to avoid a
mobile agent framework from becoming a conduit for viruses. Also implicit in the
mobile agent framework is a means for discovery.
contd..
For example, which hosts are available
for migration, and what services do they
provide? Communication is also implicit,
as agents can communicate with one
another on a host, or across hosts in
preparation for migration.
7. The mobile agent architecture is
advantageous as it supports the
development of intelligent distributed
systems. But a distributed system that is
dynamic, and whose configuration and
loading is defined by the agents
themselves.
ARCHITECTURE DESCRIPTIONS
1. Subsumption Architecture (Reactive
Architecture)
2. Behavior Networks (Reactive Architecture)
3. ATLANTIS (Deliberative Architecture)
4. Homer (Deliberative Arch)
5. BB1 (Blackboard)
6. Open Agent Architecture (Blackboard)
7. Procedural Reasoning System (BDI)
8. Aglets (Mobile)
9. Messengers (Mobile)
10. Soar (Hybrid)
SUBSUMPTION ARCHITECTURE (REACTIVE ARCHITECTURE)
1. The Subsumption architecture, originated by Rodney Brooks in the late 1980s, was created out of research in behavior-based
robotics.
2. The fundamental idea behind subsumption is that intelligent behavior can be created through a collection of simple behavior
modules. These behavior modules are collected into layers. At the bottom are behaviors that are reflexive in nature, and at the
top, behaviors that are more complex. Consider the abstract model shown in Figure.
4. At the bottom (level 0) exist the reflexive behaviors (such as obstacle avoidance). If these behaviors are required, then level 0
consumes the inputs and provides an action at the output. But no obstacles exist, so the next layer up is permitted to subsume
control.
5. At each level, a set of behaviors with different goals compete for control based on the state of the environment.
6. To support this capability levels can be inhibited (in other words, their outputs are disabled). Levels can also be suppressed
such that sensor inputs are routed to higher layers. As shown in Figure.
7. Subsumption is a parallel and distributed architecture for managing sensors and actuators. The basic premise is that we begin
with a simple set of behaviors, and once we’ve succeeded there, we extend with additional levels and higher- level behaviors.
8. For example, we begin with obstacle avoidance and then extend for object seeking. From this perspective, the architecture
takes a more evolutionary design approach.
9. Subsumption does have its problems. It is simple, but it turns out not to be extremely extensible. As new layers are added,
the layers tend to interfere with one another, and then the problem becomes how to layer the behaviors such that each has the
opportunity to control when the time is right.
10. Subsumption is also reactive in nature, meaning that in the end, the architecture still simply maps inputs to behaviors (no
planning occurs, for example). What subsumption does provide is a means to choose which behavior for a given environment
agent architecture in artificial intelligence.pptx
ATLANTIS (Deliberative Architecture)
• 1. The goal of ATLANTIS (A Three-Layer Architecture for Navigating Through Intricate Situations), was to
create a robot that could navigate through dynamic and imperfect environments in pursuit of explicitly
stated high-level goals.
• 2. ATLANTIS was to prove that a goal-oriented robot could be built from a hybrid architecture of lower-
level reactive behaviors and higher- level deliberative behaviors.
• 3. Where the subsumption architecture allows layers to subsume control, ATLANTIS operates on the
assumption that these behaviors are not exclusive of one another. The lowest layer can operate in a
reactive fashion to the immediate needs of the environment, while the uppermost layer can support
planning and more goal-oriented behaviors.
• 4. In ATLANTIS, control is performed from the bottom- up. At the lowest level (the control layer) are the
reactive behaviors.
• 5. These primitive level actions are capable of being executed first, based on the state of the
environment. At the next layer is the sequencing layer. This layer is responsible for executing plans
created by the deliberative layer.
• 6. The deliberative layer maintains an internal model of the environment and creates plans to satisfy
goals.
• 7. The sequencing layer may or may not complete the plan, based on the state of the environment. This
leaves the deliberation layer to perform the computationally expensive tasks. This is another place that
the architecture is a hybrid.
• 8. The lower- level behavior-based methods (in the controller layer) are integrated with higher- level
classical AI mechanisms (in the deliberative layer). Interestingly, the deliberative layer does not control
the sequencing layer, but instead simply advises on sequences of actions that it can perform.
• 9. The advantage of this architecture is that the low- level reactive layer and higher- level intentional
layers are asynchronous. This means that while deliberative plans are under construction, the agent is
not susceptible to the dynamic environment. This is because even though planning can take time at the
deliberative layer, the controller can deal with random events in the environment.
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx
SPEECH ACTS
Spoken human communication is used as the model for communication among
computational agents. A popular basis for analyzing human communication is speech
act theory . Speech act theory views human natural language as actions, such as
requests, suggestions, commitments, and replies. For example, when you request
something, you are not simply making a statement, but creating the request itself.
When a jury declares a defendant guilty, there is an action taken: the defendant's social
status is changed.
• A speech act has three aspects:
• 1. Locution, the physical utterance by the speaker
• 2. Illocution, the intended meaning of the utterance by the speaker
• 3. Perlocution, the action that results from the locution. KQML (Knowledge Query and
• Manipulation Language)
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx
agent architecture in artificial intelligence.pptx

More Related Content

What's hot

Constraint satisfaction Problem Artificial Intelligence
Constraint satisfaction Problem Artificial IntelligenceConstraint satisfaction Problem Artificial Intelligence
Constraint satisfaction Problem Artificial Intelligence
naeembisma
 
Unit4: Knowledge Representation
Unit4: Knowledge RepresentationUnit4: Knowledge Representation
Unit4: Knowledge Representation
Tekendra Nath Yogi
 
Data mining Measuring similarity and desimilarity
Data mining Measuring similarity and desimilarityData mining Measuring similarity and desimilarity
Data mining Measuring similarity and desimilarity
Rushali Deshmukh
 
Probabilistic Reasoning
Probabilistic ReasoningProbabilistic Reasoning
Probabilistic Reasoning
Junya Tanaka
 
Minimax
MinimaxMinimax
Minimax
sabairshad4
 
Information Extraction
Information ExtractionInformation Extraction
Information Extraction
ssbd6985
 
IOT System Management with NETCONF-YANG.pptx
IOT System Management with NETCONF-YANG.pptxIOT System Management with NETCONF-YANG.pptx
IOT System Management with NETCONF-YANG.pptx
ArchanaPandiyan
 
Problem Formulation in Artificial Inteligence Projects
Problem Formulation in Artificial Inteligence ProjectsProblem Formulation in Artificial Inteligence Projects
Problem Formulation in Artificial Inteligence Projects
Dr. C.V. Suresh Babu
 
Propositional And First-Order Logic
Propositional And First-Order LogicPropositional And First-Order Logic
Propositional And First-Order Logic
ankush_kumar
 
Dempster Shafer Theory AI CSE 8th Sem
Dempster Shafer Theory AI CSE 8th SemDempster Shafer Theory AI CSE 8th Sem
Dempster Shafer Theory AI CSE 8th Sem
DigiGurukul
 
State chart diagram
State chart diagramState chart diagram
State chart diagram
Preeti Mishra
 
Data Mining: Mining ,associations, and correlations
Data Mining: Mining ,associations, and correlationsData Mining: Mining ,associations, and correlations
Data Mining: Mining ,associations, and correlations
Datamining Tools
 
Overfitting & Underfitting
Overfitting & UnderfittingOverfitting & Underfitting
Overfitting & Underfitting
SOUMIT KAR
 
AI Lecture 6 (logical agents)
AI Lecture 6 (logical agents)AI Lecture 6 (logical agents)
AI Lecture 6 (logical agents)
Tajim Md. Niamat Ullah Akhund
 
Planning
PlanningPlanning
Planning
ahmad bassiouny
 
Knowledge representation
Knowledge representationKnowledge representation
Knowledge representation
Md. Tanvir Masud
 
Image restoration and reconstruction
Image restoration and reconstructionImage restoration and reconstruction
Feature selection concepts and methods
Feature selection concepts and methodsFeature selection concepts and methods
Feature selection concepts and methods
Reza Ramezani
 
Classical Planning
Classical PlanningClassical Planning
Classical Planning
ahmad bassiouny
 
Computer graphics basic transformation
Computer graphics basic transformationComputer graphics basic transformation
Computer graphics basic transformation
Selvakumar Gna
 

What's hot (20)

Constraint satisfaction Problem Artificial Intelligence
Constraint satisfaction Problem Artificial IntelligenceConstraint satisfaction Problem Artificial Intelligence
Constraint satisfaction Problem Artificial Intelligence
 
Unit4: Knowledge Representation
Unit4: Knowledge RepresentationUnit4: Knowledge Representation
Unit4: Knowledge Representation
 
Data mining Measuring similarity and desimilarity
Data mining Measuring similarity and desimilarityData mining Measuring similarity and desimilarity
Data mining Measuring similarity and desimilarity
 
Probabilistic Reasoning
Probabilistic ReasoningProbabilistic Reasoning
Probabilistic Reasoning
 
Minimax
MinimaxMinimax
Minimax
 
Information Extraction
Information ExtractionInformation Extraction
Information Extraction
 
IOT System Management with NETCONF-YANG.pptx
IOT System Management with NETCONF-YANG.pptxIOT System Management with NETCONF-YANG.pptx
IOT System Management with NETCONF-YANG.pptx
 
Problem Formulation in Artificial Inteligence Projects
Problem Formulation in Artificial Inteligence ProjectsProblem Formulation in Artificial Inteligence Projects
Problem Formulation in Artificial Inteligence Projects
 
Propositional And First-Order Logic
Propositional And First-Order LogicPropositional And First-Order Logic
Propositional And First-Order Logic
 
Dempster Shafer Theory AI CSE 8th Sem
Dempster Shafer Theory AI CSE 8th SemDempster Shafer Theory AI CSE 8th Sem
Dempster Shafer Theory AI CSE 8th Sem
 
State chart diagram
State chart diagramState chart diagram
State chart diagram
 
Data Mining: Mining ,associations, and correlations
Data Mining: Mining ,associations, and correlationsData Mining: Mining ,associations, and correlations
Data Mining: Mining ,associations, and correlations
 
Overfitting & Underfitting
Overfitting & UnderfittingOverfitting & Underfitting
Overfitting & Underfitting
 
AI Lecture 6 (logical agents)
AI Lecture 6 (logical agents)AI Lecture 6 (logical agents)
AI Lecture 6 (logical agents)
 
Planning
PlanningPlanning
Planning
 
Knowledge representation
Knowledge representationKnowledge representation
Knowledge representation
 
Image restoration and reconstruction
Image restoration and reconstructionImage restoration and reconstruction
Image restoration and reconstruction
 
Feature selection concepts and methods
Feature selection concepts and methodsFeature selection concepts and methods
Feature selection concepts and methods
 
Classical Planning
Classical PlanningClassical Planning
Classical Planning
 
Computer graphics basic transformation
Computer graphics basic transformationComputer graphics basic transformation
Computer graphics basic transformation
 

Similar to agent architecture in artificial intelligence.pptx

AI AND ML BASED PPT FOR THE CLARIFICATIONS ON DIFFERNT PROBLEMS
AI AND ML BASED PPT FOR THE CLARIFICATIONS ON DIFFERNT PROBLEMSAI AND ML BASED PPT FOR THE CLARIFICATIONS ON DIFFERNT PROBLEMS
AI AND ML BASED PPT FOR THE CLARIFICATIONS ON DIFFERNT PROBLEMS
Rudranshupandey1
 
Architecture for Intelligent Agents Logic-Based Architecture Logic-based arc...
Architecture for Intelligent Agents Logic-Based Architecture  Logic-based arc...Architecture for Intelligent Agents Logic-Based Architecture  Logic-based arc...
Architecture for Intelligent Agents Logic-Based Architecture Logic-based arc...
kathavera906
 
Knowledge-Based Agent in Artificial intelligence.pptx
Knowledge-Based Agent in Artificial intelligence.pptxKnowledge-Based Agent in Artificial intelligence.pptx
Knowledge-Based Agent in Artificial intelligence.pptx
suchita74
 
Unit 1
Unit 1Unit 1
81-T48
81-T4881-T48
Software engineering Questions and Answers
Software engineering Questions and AnswersSoftware engineering Questions and Answers
Software engineering Questions and Answers
Bala Ganesh
 
Simplified Cost Efficient Distributed System
Simplified Cost Efficient Distributed SystemSimplified Cost Efficient Distributed System
Simplified Cost Efficient Distributed System
Nadim Hossain Sonet
 
An agent based approach for building complex software systems
An agent based approach for building complex software systemsAn agent based approach for building complex software systems
An agent based approach for building complex software systems
Icaro Santos
 
Softwarearchitecture in practice unit1 2
Softwarearchitecture in practice unit1 2Softwarearchitecture in practice unit1 2
Softwarearchitecture in practice unit1 2
sush-sushma
 
Ch7
Ch7Ch7
Bt0081 software engineering
Bt0081 software engineeringBt0081 software engineering
Bt0081 software engineering
Techglyphs
 
Dynamically Adapting Software Components for the Grid
Dynamically Adapting Software Components for the GridDynamically Adapting Software Components for the Grid
Dynamically Adapting Software Components for the Grid
Editor IJCATR
 
Embedded multiple choice questions
Embedded multiple choice questionsEmbedded multiple choice questions
Embedded multiple choice questions
praveen kumar reddy Kavalakuntla
 
Placement management system
Placement management systemPlacement management system
Placement management system
Surya Teja
 
Sda 2
Sda   2Sda   2
Lecture-_-5-_SDA_software design and architecture.doc
Lecture-_-5-_SDA_software design and architecture.docLecture-_-5-_SDA_software design and architecture.doc
Lecture-_-5-_SDA_software design and architecture.doc
esrabilgic2
 
Design Pattern Notes: Nagpur University
Design Pattern Notes: Nagpur UniversityDesign Pattern Notes: Nagpur University
Design Pattern Notes: Nagpur University
Shubham Narkhede
 
A survey on context aware system & intelligent Middleware’s
A survey on context aware system & intelligent Middleware’sA survey on context aware system & intelligent Middleware’s
A survey on context aware system & intelligent Middleware’s
IOSR Journals
 
Ooad
OoadOoad
Ooad
OoadOoad

Similar to agent architecture in artificial intelligence.pptx (20)

AI AND ML BASED PPT FOR THE CLARIFICATIONS ON DIFFERNT PROBLEMS
AI AND ML BASED PPT FOR THE CLARIFICATIONS ON DIFFERNT PROBLEMSAI AND ML BASED PPT FOR THE CLARIFICATIONS ON DIFFERNT PROBLEMS
AI AND ML BASED PPT FOR THE CLARIFICATIONS ON DIFFERNT PROBLEMS
 
Architecture for Intelligent Agents Logic-Based Architecture Logic-based arc...
Architecture for Intelligent Agents Logic-Based Architecture  Logic-based arc...Architecture for Intelligent Agents Logic-Based Architecture  Logic-based arc...
Architecture for Intelligent Agents Logic-Based Architecture Logic-based arc...
 
Knowledge-Based Agent in Artificial intelligence.pptx
Knowledge-Based Agent in Artificial intelligence.pptxKnowledge-Based Agent in Artificial intelligence.pptx
Knowledge-Based Agent in Artificial intelligence.pptx
 
Unit 1
Unit 1Unit 1
Unit 1
 
81-T48
81-T4881-T48
81-T48
 
Software engineering Questions and Answers
Software engineering Questions and AnswersSoftware engineering Questions and Answers
Software engineering Questions and Answers
 
Simplified Cost Efficient Distributed System
Simplified Cost Efficient Distributed SystemSimplified Cost Efficient Distributed System
Simplified Cost Efficient Distributed System
 
An agent based approach for building complex software systems
An agent based approach for building complex software systemsAn agent based approach for building complex software systems
An agent based approach for building complex software systems
 
Softwarearchitecture in practice unit1 2
Softwarearchitecture in practice unit1 2Softwarearchitecture in practice unit1 2
Softwarearchitecture in practice unit1 2
 
Ch7
Ch7Ch7
Ch7
 
Bt0081 software engineering
Bt0081 software engineeringBt0081 software engineering
Bt0081 software engineering
 
Dynamically Adapting Software Components for the Grid
Dynamically Adapting Software Components for the GridDynamically Adapting Software Components for the Grid
Dynamically Adapting Software Components for the Grid
 
Embedded multiple choice questions
Embedded multiple choice questionsEmbedded multiple choice questions
Embedded multiple choice questions
 
Placement management system
Placement management systemPlacement management system
Placement management system
 
Sda 2
Sda   2Sda   2
Sda 2
 
Lecture-_-5-_SDA_software design and architecture.doc
Lecture-_-5-_SDA_software design and architecture.docLecture-_-5-_SDA_software design and architecture.doc
Lecture-_-5-_SDA_software design and architecture.doc
 
Design Pattern Notes: Nagpur University
Design Pattern Notes: Nagpur UniversityDesign Pattern Notes: Nagpur University
Design Pattern Notes: Nagpur University
 
A survey on context aware system & intelligent Middleware’s
A survey on context aware system & intelligent Middleware’sA survey on context aware system & intelligent Middleware’s
A survey on context aware system & intelligent Middleware’s
 
Ooad
OoadOoad
Ooad
 
Ooad
OoadOoad
Ooad
 

More from PriyadharshiniG41

recommendation system a topic in marketing analytics
recommendation system a topic in marketing analyticsrecommendation system a topic in marketing analytics
recommendation system a topic in marketing analytics
PriyadharshiniG41
 
understanding-cholera-a-comprehensive-analysis.pdf
understanding-cholera-a-comprehensive-analysis.pdfunderstanding-cholera-a-comprehensive-analysis.pdf
understanding-cholera-a-comprehensive-analysis.pdf
PriyadharshiniG41
 
combatting-malaria-strategies-for-prevention-and-treatment.pdf
combatting-malaria-strategies-for-prevention-and-treatment.pdfcombatting-malaria-strategies-for-prevention-and-treatment.pdf
combatting-malaria-strategies-for-prevention-and-treatment.pdf
PriyadharshiniG41
 
ant colony optimization working and explanation
ant colony optimization working and explanationant colony optimization working and explanation
ant colony optimization working and explanation
PriyadharshiniG41
 
RandomForests in artificial intelligence
RandomForests in artificial intelligenceRandomForests in artificial intelligence
RandomForests in artificial intelligence
PriyadharshiniG41
 
knowledge representation in artificial intelligence
knowledge representation in artificial intelligenceknowledge representation in artificial intelligence
knowledge representation in artificial intelligence
PriyadharshiniG41
 
information retrieval in artificial intelligence
information retrieval in artificial intelligenceinformation retrieval in artificial intelligence
information retrieval in artificial intelligence
PriyadharshiniG41
 
Inference rules in artificial intelligence
Inference rules in artificial intelligenceInference rules in artificial intelligence
Inference rules in artificial intelligence
PriyadharshiniG41
 
Unit I-Final MArketing analytics unit 1 ppt
Unit I-Final MArketing analytics unit 1 pptUnit I-Final MArketing analytics unit 1 ppt
Unit I-Final MArketing analytics unit 1 ppt
PriyadharshiniG41
 
trust,bargain,negotiate in artificail intelligence
trust,bargain,negotiate in artificail intelligencetrust,bargain,negotiate in artificail intelligence
trust,bargain,negotiate in artificail intelligence
PriyadharshiniG41
 
spirometry classification enhanced using ai
spirometry classification enhanced using aispirometry classification enhanced using ai
spirometry classification enhanced using ai
PriyadharshiniG41
 
Minmax and alpha beta pruning.pptx
Minmax and alpha beta pruning.pptxMinmax and alpha beta pruning.pptx
Minmax and alpha beta pruning.pptx
PriyadharshiniG41
 
dds.pptx
dds.pptxdds.pptx
actuators.pptx
actuators.pptxactuators.pptx
actuators.pptx
PriyadharshiniG41
 
First order logic or Predicate logic.pptx
First order logic or Predicate logic.pptxFirst order logic or Predicate logic.pptx
First order logic or Predicate logic.pptx
PriyadharshiniG41
 
14.08.2020 LKG.pdf
14.08.2020 LKG.pdf14.08.2020 LKG.pdf
14.08.2020 LKG.pdf
PriyadharshiniG41
 
Decision Tree Classification Algorithm.pptx
Decision Tree Classification Algorithm.pptxDecision Tree Classification Algorithm.pptx
Decision Tree Classification Algorithm.pptx
PriyadharshiniG41
 
problemsolving with AI.pptx
problemsolving with AI.pptxproblemsolving with AI.pptx
problemsolving with AI.pptx
PriyadharshiniG41
 
Naïve Bayes Classifier Algorithm.pptx
Naïve Bayes Classifier Algorithm.pptxNaïve Bayes Classifier Algorithm.pptx
Naïve Bayes Classifier Algorithm.pptx
PriyadharshiniG41
 
problem characterstics.pptx
problem characterstics.pptxproblem characterstics.pptx
problem characterstics.pptx
PriyadharshiniG41
 

More from PriyadharshiniG41 (20)

recommendation system a topic in marketing analytics
recommendation system a topic in marketing analyticsrecommendation system a topic in marketing analytics
recommendation system a topic in marketing analytics
 
understanding-cholera-a-comprehensive-analysis.pdf
understanding-cholera-a-comprehensive-analysis.pdfunderstanding-cholera-a-comprehensive-analysis.pdf
understanding-cholera-a-comprehensive-analysis.pdf
 
combatting-malaria-strategies-for-prevention-and-treatment.pdf
combatting-malaria-strategies-for-prevention-and-treatment.pdfcombatting-malaria-strategies-for-prevention-and-treatment.pdf
combatting-malaria-strategies-for-prevention-and-treatment.pdf
 
ant colony optimization working and explanation
ant colony optimization working and explanationant colony optimization working and explanation
ant colony optimization working and explanation
 
RandomForests in artificial intelligence
RandomForests in artificial intelligenceRandomForests in artificial intelligence
RandomForests in artificial intelligence
 
knowledge representation in artificial intelligence
knowledge representation in artificial intelligenceknowledge representation in artificial intelligence
knowledge representation in artificial intelligence
 
information retrieval in artificial intelligence
information retrieval in artificial intelligenceinformation retrieval in artificial intelligence
information retrieval in artificial intelligence
 
Inference rules in artificial intelligence
Inference rules in artificial intelligenceInference rules in artificial intelligence
Inference rules in artificial intelligence
 
Unit I-Final MArketing analytics unit 1 ppt
Unit I-Final MArketing analytics unit 1 pptUnit I-Final MArketing analytics unit 1 ppt
Unit I-Final MArketing analytics unit 1 ppt
 
trust,bargain,negotiate in artificail intelligence
trust,bargain,negotiate in artificail intelligencetrust,bargain,negotiate in artificail intelligence
trust,bargain,negotiate in artificail intelligence
 
spirometry classification enhanced using ai
spirometry classification enhanced using aispirometry classification enhanced using ai
spirometry classification enhanced using ai
 
Minmax and alpha beta pruning.pptx
Minmax and alpha beta pruning.pptxMinmax and alpha beta pruning.pptx
Minmax and alpha beta pruning.pptx
 
dds.pptx
dds.pptxdds.pptx
dds.pptx
 
actuators.pptx
actuators.pptxactuators.pptx
actuators.pptx
 
First order logic or Predicate logic.pptx
First order logic or Predicate logic.pptxFirst order logic or Predicate logic.pptx
First order logic or Predicate logic.pptx
 
14.08.2020 LKG.pdf
14.08.2020 LKG.pdf14.08.2020 LKG.pdf
14.08.2020 LKG.pdf
 
Decision Tree Classification Algorithm.pptx
Decision Tree Classification Algorithm.pptxDecision Tree Classification Algorithm.pptx
Decision Tree Classification Algorithm.pptx
 
problemsolving with AI.pptx
problemsolving with AI.pptxproblemsolving with AI.pptx
problemsolving with AI.pptx
 
Naïve Bayes Classifier Algorithm.pptx
Naïve Bayes Classifier Algorithm.pptxNaïve Bayes Classifier Algorithm.pptx
Naïve Bayes Classifier Algorithm.pptx
 
problem characterstics.pptx
problem characterstics.pptxproblem characterstics.pptx
problem characterstics.pptx
 

Recently uploaded

New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
tanupasswan6
 
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy DsouzaOpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata
 
Potential Uses of the Floyd-Warshall Algorithm as appropriate
Potential Uses of the Floyd-Warshall Algorithm as appropriatePotential Uses of the Floyd-Warshall Algorithm as appropriate
Potential Uses of the Floyd-Warshall Algorithm as appropriate
huseindihon
 
the potential of the development of the Ford–Fulkerson algorithm to solve the...
the potential of the development of the Ford–Fulkerson algorithm to solve the...the potential of the development of the Ford–Fulkerson algorithm to solve the...
the potential of the development of the Ford–Fulkerson algorithm to solve the...
huseindihon
 
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
norina2645
 
Lucknow Girls Call Indira Nagar Lucknow 08630512678 Provide Best And Top Girl...
Lucknow Girls Call Indira Nagar Lucknow 08630512678 Provide Best And Top Girl...Lucknow Girls Call Indira Nagar Lucknow 08630512678 Provide Best And Top Girl...
Lucknow Girls Call Indira Nagar Lucknow 08630512678 Provide Best And Top Girl...
bangaloreakshitakaus
 
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
dizzycaye
 
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
saadkhan1485265
 
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion data
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataTowards an Analysis-Ready, Cloud-Optimised service for FAIR fusion data
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion data
Samuel Jackson
 
potential usefulness of multi-agent maze-solving in general
potential usefulness of multi-agent maze-solving in generalpotential usefulness of multi-agent maze-solving in general
potential usefulness of multi-agent maze-solving in general
huseindihon
 
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
birajmohan012
 
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
revolutionary575
 
🚂🚘 Premium Girls Call Guwahati 🛵🚡000XX00000 💃 Choose Best And Top Girl Servi...
🚂🚘 Premium Girls Call Guwahati  🛵🚡000XX00000 💃 Choose Best And Top Girl Servi...🚂🚘 Premium Girls Call Guwahati  🛵🚡000XX00000 💃 Choose Best And Top Girl Servi...
🚂🚘 Premium Girls Call Guwahati 🛵🚡000XX00000 💃 Choose Best And Top Girl Servi...
kuldeepsharmaks8120
 
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
sharonblush
 
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
sheetal singh$A17
 
Celebrity Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service...
Celebrity Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service...Celebrity Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service...
Celebrity Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service...
tanupasswan6
 
DataScienceConcept_Kanchana_Weerasinghe.pptx
DataScienceConcept_Kanchana_Weerasinghe.pptxDataScienceConcept_Kanchana_Weerasinghe.pptx
DataScienceConcept_Kanchana_Weerasinghe.pptx
Kanchana Weerasinghe
 
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
janvikumar4133
 
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
ginni singh$A17
 
BDSM Girls Call Mumbai 👀 9820252231 👀 Cash Payment With Room DeliveryDelivery
BDSM Girls Call Mumbai 👀 9820252231 👀 Cash Payment With Room DeliveryDeliveryBDSM Girls Call Mumbai 👀 9820252231 👀 Cash Payment With Room DeliveryDelivery
BDSM Girls Call Mumbai 👀 9820252231 👀 Cash Payment With Room DeliveryDelivery
erynsouthern
 

Recently uploaded (20)

New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
New Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service And N...
 
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy DsouzaOpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
OpenMetadata Spotlight - OpenMetadata @ Aspire by Vinol Joy Dsouza
 
Potential Uses of the Floyd-Warshall Algorithm as appropriate
Potential Uses of the Floyd-Warshall Algorithm as appropriatePotential Uses of the Floyd-Warshall Algorithm as appropriate
Potential Uses of the Floyd-Warshall Algorithm as appropriate
 
the potential of the development of the Ford–Fulkerson algorithm to solve the...
the potential of the development of the Ford–Fulkerson algorithm to solve the...the potential of the development of the Ford–Fulkerson algorithm to solve the...
the potential of the development of the Ford–Fulkerson algorithm to solve the...
 
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
Mumbai Girls Call Mumbai 🛵🚡9910780858 💃 Choose Best And Top Girl Service And ...
 
Lucknow Girls Call Indira Nagar Lucknow 08630512678 Provide Best And Top Girl...
Lucknow Girls Call Indira Nagar Lucknow 08630512678 Provide Best And Top Girl...Lucknow Girls Call Indira Nagar Lucknow 08630512678 Provide Best And Top Girl...
Lucknow Girls Call Indira Nagar Lucknow 08630512678 Provide Best And Top Girl...
 
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
Female Service Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Se...
 
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
High Girls Call Nagpur 000XX00000 Provide Best And Top Girl Service And No1 i...
 
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion data
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion dataTowards an Analysis-Ready, Cloud-Optimised service for FAIR fusion data
Towards an Analysis-Ready, Cloud-Optimised service for FAIR fusion data
 
potential usefulness of multi-agent maze-solving in general
potential usefulness of multi-agent maze-solving in generalpotential usefulness of multi-agent maze-solving in general
potential usefulness of multi-agent maze-solving in general
 
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
Beautiful Girls Call Pune 000XX00000 Provide Best And Top Girl Service And No...
 
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
Celebrity Girls Call Andheri 9930245274 Unlimited Short Providing Girls Servi...
 
🚂🚘 Premium Girls Call Guwahati 🛵🚡000XX00000 💃 Choose Best And Top Girl Servi...
🚂🚘 Premium Girls Call Guwahati  🛵🚡000XX00000 💃 Choose Best And Top Girl Servi...🚂🚘 Premium Girls Call Guwahati  🛵🚡000XX00000 💃 Choose Best And Top Girl Servi...
🚂🚘 Premium Girls Call Guwahati 🛵🚡000XX00000 💃 Choose Best And Top Girl Servi...
 
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
Best Girls Call Navi Mumbai 9930245274 Provide Best And Top Girl Service And ...
 
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
Exclusive Girls Call Noida 🎈🔥9873940964 🔥💋🎈 Provide Best And Top Girl Service...
 
Celebrity Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service...
Celebrity Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service...Celebrity Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service...
Celebrity Girls Call Delhi 🎈🔥9711199171 🔥💋🎈 Provide Best And Top Girl Service...
 
DataScienceConcept_Kanchana_Weerasinghe.pptx
DataScienceConcept_Kanchana_Weerasinghe.pptxDataScienceConcept_Kanchana_Weerasinghe.pptx
DataScienceConcept_Kanchana_Weerasinghe.pptx
 
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
Beautiful Girls Call 9711199171 9711199171 Provide Best And Top Girl Service ...
 
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
Celebrity Girls Call Noida 9873940964 Unlimited Short Providing Girls Service...
 
BDSM Girls Call Mumbai 👀 9820252231 👀 Cash Payment With Room DeliveryDelivery
BDSM Girls Call Mumbai 👀 9820252231 👀 Cash Payment With Room DeliveryDeliveryBDSM Girls Call Mumbai 👀 9820252231 👀 Cash Payment With Room DeliveryDelivery
BDSM Girls Call Mumbai 👀 9820252231 👀 Cash Payment With Room DeliveryDelivery
 

agent architecture in artificial intelligence.pptx

  • 1. Architecture for intelligent agents ,Agent communication Unit 3
  • 2. DEFINITION • Agent architectures, like software architectures, are formally a description of the • elements from which a system is built and the manner in which they communicate. Further, • these elements can be defined from patterns with specific constraints. [Shaw/Garlin 1996] • 1. A number of common architectures exist that go by the names pipe-and filter or layered architecture. • 2. these define the interconnections between components. • 3. Pipe-and-Filter defines a model where data is moved through a set of one or more objects that perform a transformation. • 4. Layered simply means that the system is comprised of a set of layers that provide a specific set of logical functionality and that connectivity is commonly restricted to the layers contiguous to one another.
  • 3. TYPES OF ARCHITECTURES • Based on the goals of the agent application, a variety of agent architectures exist to • help. This section will introduce some of the major architecture types and applications for • which they can be used. 1. Reactive architectures 2. Deliberative architectures 3. Blackboard architectures 4. Belief-desire-intention (BDI) architecture 5. Hybrid architectures 6. Mobile architectures
  • 4. REACTIVE ARCHITECTURES 1. A reactive architecture is the simplest architecture for agents. 2. In this architecture, agent behaviors are simply a mapping between stimulus and response. 3. The agent has no decision-making skills, only reactions to the environment in which it exists. 4. The agent simply reads the environment and then maps the state of the environment to one or more actions. Given the environment, more than one action may be appropriate, and therefore the agent must choose. 5. The advantage of reactive architectures is that they are extremely fast. 6. This kind of architecture can be implemented easily in hardware, or fast in software lookup. 7. The disadvantage of reactive architectures is that they apply only to simpleenvironments. 8. Sequences of actions require the presence of state, which is not encoded into the mapping function.
  • 5. DELIBERATIVE ARCHITECTURES 1. A deliberative architecture, as the name implies, is one that includes some deliberation over the action to perform given the current set of inputs. 2. Instead of mapping the sensors directly to the actuators, the deliberative architecture considers the sensors, state, prior results of given actions, and other information in order to select the best action to perform. 3. The mechanism for action selection as is undefined. This is because it could be a variety of mechanisms including a production system, neural network, or any other intelligent algorithm. 4. The advantage of the deliberative architecture is that it can be used to solve much more complex problems than the reactive architecture. 5. It can perform planning, and perform sequences of actions to achieve a goal. 6. The disadvantage is that it is slower than the reactive architecture due to the deliberation for the action to select.
  • 6. BLACKBOARD ARCHITECTURES 1. The blackboard architecture is a very common architecture that is also very interesting. 2. The first blackboard architecture was HEARSAY-II, which was a speech understanding system. This architecture operates around a global work area call the blackboard. 3. The blackboard is a common work area for a number of agents that work cooperatively to solve a given problem. 4. The blackboard therefore contains information about the environment, but also intermediate work results by the cooperative agents. 5. In this example, two separate agents are used to sample the environment through the available sensors (the sensor agent) and also through the available actuators (action agent). 6. The blackboard contains the current state of the environment that is constantly updated by the sensor agent, and when an action can be performed (as specified in the blackboard), the action agent translates this action into control of the actuators.
  • 7. Contd.... 7. The control of the agent system is provided by one or more reasoning agents. 8. These agents work together to achieve the goals, which would also be contained in the blackboard. 9. In this example, the first reasoning agent could implement the goal definition behaviors, where the second reasoning agent could implement the planning portion (to translate goals into sequences of actions). 10. Since the blackboard is a common work area, coordination must be provided such that agents don’t step over one another. 11. For this reason, agents are scheduled based on their need. For example, agents canbmonitor the blackboard, and as information is added, they can request the ability toboperate. 12. The scheduler can then identify which agents desire to operate on the blackboard, and then invoke them accordingly. 13. The blackboard architecture, with its globally available work area, is easily implemented with a multi-threading system. 14. Each agent becomes one or more system threads. From this perspective, the blackboard architecture is very common for agent and non-agent systems.
  • 8. BELIEF-DESIRE-INTENTION (BDI) ARCHITECTURE 1. BDI, which stands for Belief-Desire-Intention, is an architecture that follows the theory of human reasoning as defined by Michael Bratman. 2. Belief represents the view of the world by the agent (what it believes to be the state of the environment in which it exists). Desires are the goals that define the motivation of the agent (what it wants to achieve). 3. The agent may have numerous desires, which must be consistent. Finally, Intentions specify that the agent uses the Beliefs and Desires in order to choose one or more actions in order to meet the desires. 4. As we described above, the BDI architecture defines the basic architecture of any deliberative agent. It stores a representation of the state of the environment (beliefs), maintains a set of goals (desires), and finally, an intentional element that maps desires to beliefs (to provide one or more actions that modify the state of the environment based on the agent’s needs).
  • 9. HYBRID ARCHITECTURES 1. As is the case in traditional software architecture, most architectures are hybrids. 2. For example, the architecture of a network stack is made up of a pipe-and- filter architecture and a layered architecture. 3. This same stack also shares some elements of a blackboard architecture, as there are global elements that are visible and used by each component of the architecture. 4. The same is true for agent architectures. Based on the needs of the agent system,different architectural elements can be chosen to meet those needs.
  • 10. MOBILE ARCHITECTURES 1. This architectural pattern introduces the ability for agents to migrate themselves between hosts. The agent architecture includes the mobility element, which allows an agent to migrate from one host to another. 3. An agent can migrate to any host that implements the mobile framework. 4. The mobile agent framework provides a protocol that permits communication between hosts for agent migration. 5. This framework also requires some kind of authentication and security, to avoid a mobile agent framework from becoming a conduit for viruses. Also implicit in the mobile agent framework is a means for discovery.
  • 11. contd.. For example, which hosts are available for migration, and what services do they provide? Communication is also implicit, as agents can communicate with one another on a host, or across hosts in preparation for migration. 7. The mobile agent architecture is advantageous as it supports the development of intelligent distributed systems. But a distributed system that is dynamic, and whose configuration and loading is defined by the agents themselves.
  • 12. ARCHITECTURE DESCRIPTIONS 1. Subsumption Architecture (Reactive Architecture) 2. Behavior Networks (Reactive Architecture) 3. ATLANTIS (Deliberative Architecture) 4. Homer (Deliberative Arch) 5. BB1 (Blackboard) 6. Open Agent Architecture (Blackboard) 7. Procedural Reasoning System (BDI) 8. Aglets (Mobile) 9. Messengers (Mobile) 10. Soar (Hybrid)
  • 13. SUBSUMPTION ARCHITECTURE (REACTIVE ARCHITECTURE) 1. The Subsumption architecture, originated by Rodney Brooks in the late 1980s, was created out of research in behavior-based robotics. 2. The fundamental idea behind subsumption is that intelligent behavior can be created through a collection of simple behavior modules. These behavior modules are collected into layers. At the bottom are behaviors that are reflexive in nature, and at the top, behaviors that are more complex. Consider the abstract model shown in Figure. 4. At the bottom (level 0) exist the reflexive behaviors (such as obstacle avoidance). If these behaviors are required, then level 0 consumes the inputs and provides an action at the output. But no obstacles exist, so the next layer up is permitted to subsume control. 5. At each level, a set of behaviors with different goals compete for control based on the state of the environment. 6. To support this capability levels can be inhibited (in other words, their outputs are disabled). Levels can also be suppressed such that sensor inputs are routed to higher layers. As shown in Figure. 7. Subsumption is a parallel and distributed architecture for managing sensors and actuators. The basic premise is that we begin with a simple set of behaviors, and once we’ve succeeded there, we extend with additional levels and higher- level behaviors. 8. For example, we begin with obstacle avoidance and then extend for object seeking. From this perspective, the architecture takes a more evolutionary design approach. 9. Subsumption does have its problems. It is simple, but it turns out not to be extremely extensible. As new layers are added, the layers tend to interfere with one another, and then the problem becomes how to layer the behaviors such that each has the opportunity to control when the time is right. 10. Subsumption is also reactive in nature, meaning that in the end, the architecture still simply maps inputs to behaviors (no planning occurs, for example). What subsumption does provide is a means to choose which behavior for a given environment
  • 15. ATLANTIS (Deliberative Architecture) • 1. The goal of ATLANTIS (A Three-Layer Architecture for Navigating Through Intricate Situations), was to create a robot that could navigate through dynamic and imperfect environments in pursuit of explicitly stated high-level goals. • 2. ATLANTIS was to prove that a goal-oriented robot could be built from a hybrid architecture of lower- level reactive behaviors and higher- level deliberative behaviors. • 3. Where the subsumption architecture allows layers to subsume control, ATLANTIS operates on the assumption that these behaviors are not exclusive of one another. The lowest layer can operate in a reactive fashion to the immediate needs of the environment, while the uppermost layer can support planning and more goal-oriented behaviors. • 4. In ATLANTIS, control is performed from the bottom- up. At the lowest level (the control layer) are the reactive behaviors. • 5. These primitive level actions are capable of being executed first, based on the state of the environment. At the next layer is the sequencing layer. This layer is responsible for executing plans created by the deliberative layer. • 6. The deliberative layer maintains an internal model of the environment and creates plans to satisfy goals. • 7. The sequencing layer may or may not complete the plan, based on the state of the environment. This leaves the deliberation layer to perform the computationally expensive tasks. This is another place that the architecture is a hybrid. • 8. The lower- level behavior-based methods (in the controller layer) are integrated with higher- level classical AI mechanisms (in the deliberative layer). Interestingly, the deliberative layer does not control the sequencing layer, but instead simply advises on sequences of actions that it can perform. • 9. The advantage of this architecture is that the low- level reactive layer and higher- level intentional layers are asynchronous. This means that while deliberative plans are under construction, the agent is not susceptible to the dynamic environment. This is because even though planning can take time at the deliberative layer, the controller can deal with random events in the environment.
  • 22. SPEECH ACTS Spoken human communication is used as the model for communication among computational agents. A popular basis for analyzing human communication is speech act theory . Speech act theory views human natural language as actions, such as requests, suggestions, commitments, and replies. For example, when you request something, you are not simply making a statement, but creating the request itself. When a jury declares a defendant guilty, there is an action taken: the defendant's social status is changed. • A speech act has three aspects: • 1. Locution, the physical utterance by the speaker • 2. Illocution, the intended meaning of the utterance by the speaker • 3. Perlocution, the action that results from the locution. KQML (Knowledge Query and • Manipulation Language)