Agent Mediated Grid Agent Mediated Grid Services in e-LearningServices in e-Learning

Chun Yan, MiaoChun Yan, Miao

School of Computer EngineeringSchool of Computer Engineering

Nanyang Technological University (NTU) Nanyang Technological University (NTU)

Singapore 639798Singapore 639798

April, 2004April, 2004

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Singapore

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Preface: AgentPreface: Agent

Agent is an emerging new paradigm for Agent is an emerging new paradigm for next generation e-Systems in various next generation e-Systems in various domains.domains.Agent technology is identified by “MIT Agent technology is identified by “MIT Technology Review” as one of the Technology Review” as one of the technologies that will change the world”.technologies that will change the world”.It is predicted that in 10 years time, most It is predicted that in 10 years time, most new software developments will contain new software developments will contain embedded agent systems. embedded agent systems.

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2020 Vision: How People Learn2020 Vision: How People Learn

The familiar “The familiar “world to the desktopworld to the desktop””

““Alice in WonderlandAlice in Wonderland”: computer based ”: computer based agents assist learners in diverse ways agents assist learners in diverse ways

““ubiquitous learningubiquitous learning”: embedded agents in ”: embedded agents in handheld wireless devices and in real handheld wireless devices and in real objects i.e. intelligent objects objects i.e. intelligent objects

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Preface: Goal OrientationPreface: Goal Orientation

An agent is defined as the one who acts on behalf An agent is defined as the one who acts on behalf of human beings. of human beings. By nature, a human being does things based on By nature, a human being does things based on goals. Goal orientation is a key character of agents. goals. Goal orientation is a key character of agents. A large majority of the current efforts on agent A large majority of the current efforts on agent modeling and development still employ object-modeling and development still employ object-oriented methodologies, which model oriented methodologies, which model an agent as an agent as an extended objectan extended objectAgents are goal oriented, which Agents are goal oriented, which necessitates a shift necessitates a shift in modelling paradigmin modelling paradigm, from object-oriented , from object-oriented modeling to goal-oriented modeling.modeling to goal-oriented modeling.

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AgendaAgenda

Agent Mediate Service Oriented GridAgent Mediate Service Oriented Grid

Goal Net: A Goal oriented modeling Goal Net: A Goal oriented modeling approach to agent oriented systemsapproach to agent oriented systems

Goal Selection and Action SelectionGoal Selection and Action Selection

Modeling of MAS with Goal NetModeling of MAS with Goal Net

Goal Autonomous AgentGoal Autonomous Agent

Agent Mediate Grid Service in e-LearningAgent Mediate Grid Service in e-Learning

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Agent’s Goal ModelAgent’s Goal Model

Task OrientedTask Oriented: : an agent lives in a task-oriented domain; the goal of an agent is a set of tasks to perform.

State OrientedState Oriented: : an agent lives in the state-oriented domain. The agent’s environment is evolved with a finite set of states. A goal of an agent is a desired state that the agent tries to reach from its current state by going through a sequence of states.

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Characterizing Agent’s GoalCharacterizing Agent’s Goal

To model the complex goals of agents, a To model the complex goals of agents, a characterization of an agent’s goal with characterization of an agent’s goal with different properties such as composite different properties such as composite goal, fuzzy goal, partial goal, sub goal etc. goal, fuzzy goal, partial goal, sub goal etc. is highly needed. is highly needed.

To enable agents to present such goal To enable agents to present such goal characters, new goal models are characters, new goal models are demanded.demanded.

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Goal Net: OverviewGoal Net: OverviewA Goal Net is composed of five basic A Goal Net is composed of five basic objects: objects: statesstates, , transitionstransitions, , arcsarcs, , branchesbranches, , and and tokenstokens. .

composite state

transition

atomic state

transition

composite state

arc

token

branch

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Goal Net: StateGoal Net: State

StateState State is a system situation at a time during State is a system situation at a time during

agent runningagent running Atomic, CompositeAtomic, Composite Goal is a desired state that an agent intents to Goal is a desired state that an agent intents to

reach.reach. In a Goal Net, a composite state is a goal.In a Goal Net, a composite state is a goal.

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Goal Net: TransitionGoal Net: Transition

TransitionTransition Defines actions to transit from one state to Defines actions to transit from one state to

another state.another state. Defines action selection mechanismDefines action selection mechanism Direct, Conditional, Probabilistic, FuzzyDirect, Conditional, Probabilistic, Fuzzy

Direct Transition

Probabilistic Transition

Conditional Transition

Fuzzy Cognitive Transition

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Goal Net: TransitionGoal Net: Transition

Transitions can represent four basic Transitions can represent four basic relationships between states:relationships between states: sequence, sequence, conflict, concurrency, and synchronizationconflict, concurrency, and synchronization. .

Si

Concurrency

Synchronization

Sequence

Choice

SjSi

Si

SiSk

Sj

Sk

Sj

Sk

Sj

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Goal Net: Arc,Token,BranchGoal Net: Arc,Token,BranchAn An arcarc is used to connect a state to a is used to connect a state to a transition or a transition to a state. It transition or a transition to a state. It indicates the relationship between the indicates the relationship between the state and the transition it connects.state and the transition it connects.A A tokentoken is used to indicate agent’s is used to indicate agent’s current activities in different states. It current activities in different states. It presents dynamic behaviors of the goal presents dynamic behaviors of the goal model. It indicates the progress of the model. It indicates the progress of the goal pursuit process.goal pursuit process.The The branchesbranches are used to represent are used to represent the decomposition of a composite the decomposition of a composite state.state.

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Goal Net: MeasurementGoal Net: Measurement

Goal MeasurementGoal Measurement AchievementAchievement: represents a recognizable : represents a recognizable

benefit of reaching a goal;benefit of reaching a goal; Distance: indicates how close the current Distance: indicates how close the current

state is to a composite state or a sub goal; state is to a composite state or a sub goal; CompletenessCompleteness: represents a percentage of : represents a percentage of

the entire goal fulfillment; the entire goal fulfillment; CostCost: means the time, memory, money, etc. : means the time, memory, money, etc.

spent or required to be spent from one state spent or required to be spent from one state to another. to another.

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Goal Net: Goal/Action SelectionGoal Net: Goal/Action Selection

Goal Selection – Goal Selection – Goal AutonomyGoal Autonomy Take future goals/stats into considerationTake future goals/stats into consideration Achievement, Cost, Constraint, Trust, IndexAchievement, Cost, Constraint, Trust, Index

Action Selection – Action Selection – Behavior AutonomyBehavior Autonomy Sequential execution Sequential execution Situation Action: Rule-based inferenceSituation Action: Rule-based inference Probabilistic inferenceProbabilistic inference Fuzzy Cognitive InferenceFuzzy Cognitive Inference

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Goal Net: Action SelectionGoal Net: Action Selection Sequential executionSequential execution: This is the simplest : This is the simplest

situation. There is no action selection needed. situation. There is no action selection needed. Agents can move from one state to the next Agents can move from one state to the next state by the execution of the fixed sequence of state by the execution of the fixed sequence of actions. actions.

Rule-based inferenceRule-based inference: In this situation, : In this situation, complete information for action selection is complete information for action selection is present. Agents can make decision according present. Agents can make decision according to the rules and current values of all the factors to the rules and current values of all the factors or states. or states.

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Goal Net: Action SelectionGoal Net: Action Selection Probabilistic inferenceProbabilistic inference: In this situation, information for : In this situation, information for

action selection is not complete. A Bayesian network action selection is not complete. A Bayesian network

that represents the relationships between factors and that represents the relationships between factors and

actions can be constructed. An agent then reasons its actions can be constructed. An agent then reasons its

actions through the Bayesian network inference. actions through the Bayesian network inference.

Fuzzy Cognitive InferenceFuzzy Cognitive Inference: A Fuzzy Cognitive map that : A Fuzzy Cognitive map that

represents the relationships between factors and represents the relationships between factors and

actions can be constructed. An agent reasons its actions can be constructed. An agent reasons its

actions through fuzzy cognitive inference. actions through fuzzy cognitive inference.

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Modeling MAS with Goal NetModeling MAS with Goal Net

In addition to an agent goal model, Goal Net also In addition to an agent goal model, Goal Net also serves as a goal-oriented requirement and serves as a goal-oriented requirement and modeling tool, and a multi-agent identification, modeling tool, and a multi-agent identification, organization and coordination model.organization and coordination model. From Goal Hierarchy to Agent HierarchyFrom Goal Hierarchy to Agent Hierarchy Agent IdentificationAgent Identification Agent CoordinationAgent Coordination

Goal Net is able to assist in whole life cycle for Goal Net is able to assist in whole life cycle for development of agent-oriented applicationsdevelopment of agent-oriented applications

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Agent Mediated Service Oriented Grid Agent Mediated Service Oriented Grid

7. End Users

6.Consumer Applications

5. Service Agents

4. Information Service Center

3. Marketing Service Agents

2. Grid Services

1. Provider Applications

Applications Applications

Applications

A

A

AA A

AA

A

A

AA

Services Services

Applications

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Marketing Agents in Agent GridMarketing Agents in Agent Grid

Provide Service

Received Request

Process Request

Sent Results

Negotiated Job Dispatched

Exception Processed

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Service Agents in Agent GridService Agents in Agent Grid

Serviced

Request Received

Obtain Service

Service Located

Services Discovered

Search Service

Broadcast

Service Selected

Negotiated Request Sent

Services Found

Query Prepared

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From Goal Hierarchy to Agent Hierarchy: From Goal Hierarchy to Agent Hierarchy: MAS DerivationMAS Derivation

X

Y

Z

Marketing agent

Process request agentNegotiate agent

D

B

A

Negotiation agent

Lookup agent

Locate service agent

Service agent

C

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Goal Autonomous AgentGoal Autonomous Agent

The agent whose goal is modeled with The agent whose goal is modeled with the Goal Net is able to present both the Goal Net is able to present both behavior autonomy and goal autonomy behavior autonomy and goal autonomy in a dynamic changing environment. We in a dynamic changing environment. We call this type of agents call this type of agents goal autonomous goal autonomous agentsagents..

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Goal Autonomous Agent Life CycleGoal Autonomous Agent Life Cycle

PRPR22AA PerceivePerceive: The agent perceives its environment : The agent perceives its environment

continuously to sense any new situations.continuously to sense any new situations. Reason for goal selectionReason for goal selection: The agent infers the : The agent infers the

next goal, based on its goal model, knowledge, and next goal, based on its goal model, knowledge, and the perception of its environment.the perception of its environment.

Reason for action selectionReason for action selection: The agent infers : The agent infers actions based on the selected goal, knowledge, and actions based on the selected goal, knowledge, and the perception of its environment.the perception of its environment.

ActAct: The selected actions are executed.: The selected actions are executed.

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Goal Autonomous AgentGoal Autonomous Agent

Agent ModelAgent Model

DatabaseKnowledge

Base

Data Goal Knowledge

InferenceEngine

controller

communication

percep

tion action

En

viron

men

t

En

viron

men

t

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E-Learning SystemE-Learning System

E-learning service

Learner preparation Learning

Role Selected

Pre-Assessment

Learning Path Generated

Learning Object Delivery

Teaching

Post-Assessment

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E-Learning Grid Services SystemE-Learning Grid Services System

Learning agent

Courseware servers in a Grid environment

Learning service agent

Learner preparation agent

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E-Learning Grid Services SystemE-Learning Grid Services System

Learning agent

Courseware servers in a Grid environment

Learning service agent

Learner preparation agent

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ConclusionConclusionGoal Net serves as a goal-oriented modeling and Goal Net serves as a goal-oriented modeling and analysis tool, an agent goal model, and a multi-analysis tool, an agent goal model, and a multi-agent modeling, identification and organization agent modeling, identification and organization model. model. As a new agent goal model, Goal Net enables As a new agent goal model, Goal Net enables the agents to present both behavior autonomy the agents to present both behavior autonomy and goal autonomy.and goal autonomy.The modeling and design of goal autonomous The modeling and design of goal autonomous multi-agent systems using Goal Net have multi-agent systems using Goal Net have demonstrated a promising approach for demonstrated a promising approach for designing and developing intelligent, open designing and developing intelligent, open distributed agent systems in grid service in e-distributed agent systems in grid service in e-Learning. Learning.

Questions?Questions?