Download - Agents in QuizMASter

Multi-agent Architecture for Integrating Adaptive Features in Immersive 3D Virtual Learning Environments

Agents in QuizMASter

Intelligent Education Systems GroupDr Fuhua Lin7/25/20111Fuhua Lin, SCIS, FST, Athabasca University1UT Austin Villa Wins World RoboCup Championships 2011

http://www.utexas.edu/news/2011/07/19/villa_wins/The key to victory, says Peter Stone, was that he and his graduate and undergraduate students taught their robots to teach themselves. 7/25/20112Fuhua Lin, SCIS, FST, Athabasca UniversityThe University of Texas at Austin's 2011 RoboCupSoccer team, called UT Austin Villa, won the championship in the three-dimensional simulation division. The UT Austin Villa team, which was led by University of Texas at Austin computer scientists Peter Stone and Patrick MacAlpine, beat 21 other teams from 11 countries, scoring 136 goals and allowing none. The key to victory was teaching the robots to teach themselves, Stone says. "We used the distributed computing cluster here in the department, which has hundreds of computers, in order to do machine learning," he says. The simulation division is the only one of the five divisions in the tournament that does not use real manufactured robots. Instead, the games are played by two teams of nine autonomous artificial intelligence programs, which play the game in a video game-like environment. Each player has to react, in real time, to the data being sent to it by its teammates and by the simulator, which models the physics of the real world and is constantly recalculating its datastream based on where the 18 different players are going and what they do.

Figure 1: Screenshot from the 3-D simulation program used in RoboCupSoccer, generated by the Spark Generic Physical Multiagent Simulator (SimSpark).

Figure 2: The "Nao," which is the virtual robot used in RoboCupSoccer, generated by the Spark Generic Physical Multiagent Simulator (SimSpark). 2

Immersive Learning EnvironmentsCommercial platforms such as:World of Warcraft for online gamingSecond Life for online social networkingPositive outcomes of these environmentsa high level of realismassociated levels of engagementsupporting and encouraging social interactionWhether these positive outcomes can be generalized and applied to the education community and weather institution can adopt these environments and provide them as part of their online ICT infrastructure ?

7/25/20113Fuhua Lin, SCIS, FST, Athabasca UniversityGame-based E-Learningthe use of a computer games based approach to deliver, support, and enhance teaching, learning, assessment, and evaluation. (Connolly et al., 2004)

7/25/20114Fuhua Lin, SCIS, FST, Athabasca UniversityGoalDevise virtual learning environments that integrate AI and game engines. 3Es: Effective, Efficient, Engaging3Is: Intelligent, Interactive, ImmersiveAdaptiveMotivational elements: 4Cs: challenge, curiosity, control, and context, creativity is the new emerging C a (Lepper & Henderlong, 2000)Social relationshipPlay and learn Target users: For all learners, both within and outside of the classroom. What to do? Infrastructure for Building Virtual ClassroomsResearch Development Evaluation 7/25/20115Fuhua Lin, SCIS, FST, Athabasca UniversityWe are particularly interested in devising learning environments that integrate artificial intelligence and commercial game engines. Our learning environments use AI computational frameworks to create the most effective, efficient, and engaging learning interactions for all learners, both within and outside of the classroom. To this end, we are equally interested in research (with a focus on creating new learning technologies), development (with a focus on building robust learning technologies that are deployed in school systems and informal learning settings), and evaluation (with a focus on testing learning technologies to determine precisely which technologies under which conditions provide the greatest learning gains and deepest, most engaging learning experiences).5Requirements (1) --- BelievabilityBody Language how to detect ithow to express it adaptively and automatically.

7/25/20116Fuhua Lin, SCIS, FST, Athabasca University

The believability of what we communicate is influenced 55% by body language For a six month baby, when you smile to him/her, he/she may smile to you.

Research in communication has consistently shown that messages are conveyed in many ways besides words. Dr. Albert Mehrabian stated in his book Silent Messages that the believability of what we communicate is influenced 7% by words, 38% by tones of voice and 55% by body language.

Simulating Human communicative strategiesCompose explanations spoken or textual Deliver encouragement or critiquesMaintain a mixed initiative dialogueAnalyze a student explanation, spoken or textual Question students approachRecognize students affect (emotion), focus of attention, or motivationEngage students in role playing; hire partners for training interactive skills (Woolf, 2009) 6Requirements (2) ---- Autonomy Game engines are still too complex for most educators to implement their own learning games. avatar animation and facial expressions, but these features must be controlled manually there is no way to associate them with game events. stops short of providing tools essential to many educational game activities, such as question banks, score keeping, and user modeling.

7/25/20117Fuhua Lin, SCIS, FST, Athabasca UniversityAlthough Wonderland is a substantial improvement over the JME engine in terms of making 3DVW technology available more accessible to educators and content developers, the Wonderland toolkit is still too complex for most educators to implement their own learning games. As an example, the most recent version of Wonderland provides initial support for avatar animation and facial expressions, but these features must be controlled manually there is no way to associate them with game events. In addition, the toolkit stops short of providing tools essential to many educational game activities, such as question banks, score keeping, and user modeling. These would have to be provided by the game developer, and would require considerable programming expertise to interface with the Wonderland toolkit. Again this is beyond the capabilities of many educators. The Wonderland toolkit, therefore, offers only a partial solution to the complexity problem of creating 3DVW educational games.

7Requirements (3) Easy to BuildThe time and expertise required to create believable NPCs and engaging learning activities based on virtual-world technologies remains a significant barrier.

How to incorporate intelligence into NPCs?How to make the agents learn?7/25/20118Fuhua Lin, SCIS, FST, Athabasca UniversityHowever, significant challenges exist in the widespread adoption of immersive virtual worlds to the educational field. 8

Why Agents and MAS? Complexity it is not practical to analyse and code for every possible game state and every possible interaction between the various game elements. Why Multiagent Systems? For cooperative problem solving.For emergent behaviours.For global optimization....

7/25/20119Fuhua Lin, SCIS, FST, Athabasca UniversityOne strong argument in favour of agent-based game control is

Because of the potential complexity of the environment, it is not practical to analyse and code for every possible game state and every possible interaction between the various game elements. The controlling agents be capable of acting autonomously, making their own decisions to resolve the apparent conflict and to act in their own interests and the best interests of the game. 9AgentsAgentGoal-orientedagentReactiveagent7/25/201110Fuhua Lin, SCIS, FST, Athabasca UniversityAgent: Support a relationship so that humans can establish trust in the behavior of their agents and intervene in the decision process at any time. Avatar: a movable image that represents a person in a virtual reality environment or in cyberspace

What types of agent architecture are most suitable to implement intelligent behavior in games?Learner Agents (Emotional state model, Game data such as the users score, score relative to other players ProfilesPedagogical AgentsNPCs (e.g. virtual co-learners)Other System agents (e.g., yellow page, launching, killing agents System Level AgentsThe system agents will be used as a means of providing basic game functionality such as scorekeeping, question and answer interactions, controlling overall game behaviour, and automating various tasks related to game-play. Agent-Controlled NPCs will also be used to support emotional expression in the interactions with Wonderland game charactersto provide pedagogical functions such as the ability of a game element to provide appropriate feedback. 10AgentsAgentNPCAgentSystem AgentStudent AgentGoal-orientedagentReactiveagent7/25/201111Fuhua Lin, SCIS, FST, Athabasca UniversityAgent: Support a relationship so that humans can establish trust in the behavior of their agents and intervene in the decision process at any time. Avatar: a movable image that represents a person in a virtual reality environment or in cyberspace

What types of agent architecture are most suitable to implement intelligent behavior in games?Learner Agents (Emotional state model, Game data such as the users score, score relative to other players ProfilesPedagogical AgentsNPCs (e.g. virtual co-learners)Other System agents (e.g., yellow page, launching, killing agents System Level AgentsThe system agents will be used as a means of providing basic game functionality such as scorekeeping, question and answer interactions, controlling overall game behaviour, and automating various tasks related to game-play. Agent-Controlled NPCs will also be used to support emotional expression in the interactions with Wonderland game charactersto provide pedagogical functions such as the ability of a game element to provide appropriate feedback. 11AgentsAgentNPCAgentSystem AgentStudent AgentGoal-orientedagentReactiveagentAvatarNPC7/25/201112Fuhua Lin, SCIS, FST, Athabasca UniversityAgent: Support a relationship so that humans can establish trust in the behavior of their agents and intervene in the decision process at any time. Avatar: a movable image that represents a person in a virtual reality environment or in cyberspace

What types of agent architecture are most suitable to implement intelligent behavior in games?Learner Agents (Emotional state model, Game data such as the users score, score relative to other players ProfilesPedagogical AgentsNPCs (e.g. virtual co-learners)Other System agents (e.g., yellow page, launching, killing agents System Level AgentsThe system agents will be used as a means of providing basic game functionality such as scorekeeping, question and answer interactions, controlling overall game behaviour, and automating various tasks related to game-play. Agent-Controlled NPCs will also be used to support emotional expression in the interactions with Wonderland game charactersto provide pedagogical functions such as the ability of a game element to provide appropriate feedback. 12AgentsAgentNPCAgentSystem AgentPedagogical AgentVirtual StudentVirtual AudienceStudent AgentTimerScorekeeperAMAGoal-orientedagentReactiveagentAvatarNPC7/25/201113Fuhua Lin, SCIS, FST, Athabasca UniversityAgent: Support a relationship so that humans can establish trust in the behavior of their agents and intervene in the decision process at any time. Avatar: a movable image that represents a person in a virtual reality environment or in cyberspace

What types of agent architecture are most suitable to implement intelligent behavior in games?Learner Agents (Emotional state model, Game data such as the users score, score relative to other players ProfilesPedagogical AgentsNPCs (e.g. virtual co-learners)Other System agents (e.g., yellow page, launching, killing agents System Level AgentsThe system agents will be used as a means of providing basic game functionality such as scorekeeping, question and answer interactions, controlling overall game behaviour, and automating various tasks related to game-play. Agent-Controlled NPCs will also be used to support emotional expression in the interactions with Wonderland game charactersto provide pedagogical functions such as the ability of a game element to provide appropriate feedback. 13AgentsAgentNPCAgentSystem AgentPedagogical AgentVirtual StudentVirtual AudienceStudent AgentTimerScorekeeperAMAGoal-orientedagentReactiveagentAvatarNPCTasks 1. Expressing verbal/non-verbal communication actions2. Learning from the student agents about the students 3. Generating quizzes and hints 4. Reasoning about the situation.5. Providing narrative and dialogue functions that provide increased engagement and immersion in the environment14Agent: Support a relationship so that humans can establish trust in the behavior of their agents and intervene in the decision process at any time. Avatar: a movable image that represents a person in a virtual reality environment or in cyberspace

What types of agent architecture are most suitable to implement intelligent behavior in games?Learner Agents (Emotional state model, Game data such as the users score, score relative to other players ProfilesPedagogical AgentsNPCs (e.g. virtual co-learners)Other System agents (e.g., yellow page, launching, killing agents System Level AgentsThe system agents will be used as a means of providing basic game functionality such as scorekeeping, question and answer interactions, controlling overall game behaviour, and automating various tasks related to game-play. Agent-Controlled NPCs will also be used to support emotional expression in the interactions with Wonderland game charactersto provide pedagogical functions such as the ability of a game element to provide appropriate feedback. 14AgentsAgentNPCAgentSystem AgentPedagogical AgentVirtual StudentVirtual AudienceStudent AgentTimerScorekeeperAMAGoal-orientedagentReactiveagentAvatarNPCTasks1. Expressing verbal/non-verbal communication actions Adaptively to increase user engagement level. 2. Learning from the student agents about the students 3. Generating quizzes and hints 4. Reasoning about the situation.5. Providing narrative and dialogue functions that provide increased engagement and immersion in the environmentTSI-enhanced 15Agent: Support a relationship so that humans can establish trust in the behavior of their agents and intervene in the decision process at any time. Avatar: a movable image that represents a person in a virtual reality environment or in cyberspace

What types of agent architecture are most suitable to implement intelligent behavior in games?Learner Agents (Emotional state model, Game data such as the users score, score relative to other players ProfilesPedagogical AgentsNPCs (e.g. virtual co-learners)Other System agents (e.g., yellow page, launching, killing agents System Level AgentsThe system agents will be used as a means of providing basic game functionality such as scorekeeping, question and answer interactions, controlling overall game behaviour, and automating various tasks related to game-play. Agent-Controlled NPCs will also be used to support emotional expression in the interactions with Wonderland game charactersto provide pedagogical functions such as the ability of a game element to provide appropriate feedback. 15AgentsAgentNPCAgentSystem AgentPedagogical AgentVirtual StudentVirtual AudienceStudent AgentTimerScorekeeperTasks:1. Detecting and analyzing verbal/non-verbal communication and behavior(e.g. emotion, gesture)2. Learning from other agents 3. Modeling student (level, preferences, learning styles, profiles)4. Identifying social relations

AMAGoal-orientedagentReactiveagentAvatarNPCTSI-enhanced 16Tasks1. Expressing verbal/non-verbal communication actions Adaptively to increase user engagement level. 2. Learning from the student agents about the students 3. Generating quizzes and hints 4. Reasoning about the situation.5. Providing narrative and dialogue functions that provide increased engagement and immersion in the environmentAgent: Support a relationship so that humans can establish trust in the behavior of their agents and intervene in the decision process at any time. Avatar: a movable image that represents a person in a virtual reality environment or in cyberspace

What types of agent architecture are most suitable to implement intelligent behavior in games?Learner Agents (Emotional state model, Game data such as the users score, score relative to other players ProfilesPedagogical AgentsNPCs (e.g. virtual co-learners)Other System agents (e.g., yellow page, launching, killing agents System Level AgentsThe system agents will be used as a means of providing basic game functionality such as scorekeeping, question and answer interactions, controlling overall game behaviour, and automating various tasks related to game-play. Agent-Controlled NPCs will also be used to support emotional expression in the interactions with Wonderland game charactersto provide pedagogical functions such as the ability of a game element to provide appropriate feedback. 16AgentsAgentNPCAgentSystem AgentPedagogical AgentVirtual StudentVirtual AudienceStudent AgentTimerScorekeeperTasks:1. Detecting and analyzing verbal/non-verbal communication and behavior(e.g. emotion, gesture)2. Learning from other agents 3. Modeling student (level, preferences, learning styles, profiles)4. Identifying social relations

AMAGoal-orientedagentReactiveagentAvatarNPCTSI-enhanced Artifact17Tasks1. Expressing verbal/non-verbal communication actions Adaptively to increase user engagement level. 2. Learning from the student agents about the students 3. Generating quizzes and hints 4. Reasoning about the situation.5. Providing narrative and dialogue functions that provide increased engagement and immersion in the environmentAgent: Support a relationship so that humans can establish trust in the behavior of their agents and intervene in the decision process at any time. Avatar: a movable image that represents a person in a virtual reality environment or in cyberspace

What types of agent architecture are most suitable to implement intelligent behavior in games?Learner Agents (Emotional state model, Game data such as the users score, score relative to other players ProfilesPedagogical AgentsNPCs (e.g. virtual co-learners)Other System agents (e.g., yellow page, launching, killing agents System Level AgentsThe system agents will be used as a means of providing basic game functionality such as scorekeeping, question and answer interactions, controlling overall game behaviour, and automating various tasks related to game-play. Agent-Controlled NPCs will also be used to support emotional expression in the interactions with Wonderland game charactersto provide pedagogical functions such as the ability of a game element to provide appropriate feedback. 17Environment Programming in MAS

Environment 7/25/201118Fuhua Lin, SCIS, FST, Athabasca UniversityFig. 1 provides an overview of the main concepts characterising artifact-based environments.The environment is conceived as a dynamic set of computational entities called artifacts, representing in general resources and tools that agents working in the same environment canshare and exploit. The overall set of artifacts can be organized in one or multiple workspaces, possibly distributed in different network nodes. A workspace represents a place (followingthe terminology introduced in Section 2), the locus of one or multiple activities involving a set of agents and artifacts.

From the MAS designer and programmer viewpoint, the notion of artifact is a first class abstraction, the basic module to structure and organise the environment, providing ageneral-purpose programming and computational model to shape the kind of the functionalities available to agents. Actually, MAS programmers define types of artifacts, analogouslyto classes in OOP, which define the structure and behaviour of the concrete instances of those types.

Each workspace is meant to have a (dynamic) set of artifact types that can be used to create instances.

From the agent viewpoint, artifacts are the first-class entities structuring, from a functional point of view, the computational world where they are situated and that they can create, share, use, perceive at runtime. To make its functionalities available and exploitable by agents, an artifact provides a set of operations and a set of observable properties (see Fig. 2). Operations represent computational processes possibly long-term executed inside artifacts, that can be triggered by agents or other artifacts. The term usage interface is used to indicate the overall set of artifact operations available to agents. Observable properties represent state variables whose value can be perceived by agents3; the value of an observable property can change dynamically, as result of operation execution. The execution of an operation can generate also signals, to 18Environment Programming in MAS




From the agent viewpoint, artifacts are the first-class entities structuring, from a functional point of view, the computational world where they are situated and that they can create, share, use, perceive at runtime. To make its functionalities available and exploitable by agents, an artifact provides a set of operations and a set of observable properties (see Fig. 2). Operations represent computational processes possibly long-term executed inside artifacts, that can be triggered by agents or other artifacts. The term usage interface is used to indicate the overall set of artifact operations available to agents. Observable properties represent state variables whose value can be perceived by agents3; the value of an observable property can change dynamically, as result of operation execution. The execution of an operation can generate also signals, to 19Environment Programming in MAS




From the agent viewpoint, artifacts are the first-class entities structuring, from a functional point of view, the computational world where they are situated and that they can create, share, use, perceive at runtime. To make its functionalities available and exploitable by agents, an artifact provides a set of operations and a set of observable properties (see Fig. 2). Operations represent computational processes possibly long-term executed inside artifacts, that can be triggered by agents or other artifacts. The term usage interface is used to indicate the overall set of artifact operations available to agents. Observable properties represent state variables whose value can be perceived by agents3; the value of an observable property can change dynamically, as result of operation execution. The execution of an operation can generate also signals, to 20Reactive AgentsPerceive eventsSimple set of rules event action (i.e., activation of a specific behavior)Actions are often known as behavioursExample of a simple mail agent:if send mail then check virusIf new mail then check spamIf spam then send message to friends agentsIf new message then get new spam informationPros:simple and efficientCons:Action depending only on stimuliNot flexibleNot really autonomous7/25/2011Fuhua Lin, SCIS, FST, Athabasca University21

Reactive Agents with StateInternal state (internal knowledge)Update of internal stateNew state = actual perception + old stateThe update may requireKnowledge on how the world evolves which can also dynamically acquired by the agentKnowledge on how the agent actions influence the worldSelect action (i.e., behavior) accordingly7/25/2011Fuhua Lin, SCIS, FST, Athabasca University22

An object is a sort of reactive agents, but- It has no rule for action selection- It actions are directly commanded by the external ExampleA mail agents that keeps track of the users marking some messages as spams and take these into account in future actions

Goal-oriented agentsGoal a desired situation to eventually achieveThe agent exploits the goal and its knowledge select actions whose effect would be that of approaching the goalHow can a goal be selected?Search in the state spacePlanningsHeuristics sub-optimal actions7/25/2011Fuhua Lin, SCIS, FST, Athabasca University23

Example: an agent to minimize fragmentation in a hard disk- Knapsack problem- Do not know the future but know the past- Select allocation of new files based on some heuristics- An action does not necessarily minimize the current fragmentation- Perform de-fragmentation action when the computer is idle

Utility-oriented Agents The Goal is that of maximizing the current utilityopportunistic behaviorUtilityA function of some parameter, measuring the state of goodness (with respect to the agent) of a situationOften, it measures a trade-off between contrasting objectivesExampleAn agent to maximize CPU utilizationAlways select the ready processThe current choice may be sub-optimal with regard to the global execution time of processes 7/25/2011Fuhua Lin, SCIS, FST, Athabasca University24

24Hybrid ArchitecturesMixing utility and goalsAn agent that has to achieve a goal and, at the same time, has to maximize a specific utility functionTrade-off between the two goals, which may be contrastingOften, the various ways to approach a goal can be quantified by a utility functionDo the actions that approach the goal with the maximal utilityMixing reactive and goal-oriented behaviorA long terms goal that include several short term actions on the environmentThat could lead to sub-optimal choices7/25/2011Fuhua Lin, SCIS, FST, Athabasca University25ABDI agent model7/25/2011Fuhua Lin, SCIS, FST, Athabasca University26BeliefsIntentionsPlan libraryDesiresInterpreter

Sensor inputAction outputAgent BDI agents are most suitable to implement intelligent behavior in games The use of goal-oriented action planning in gaming They make explicit use of goals and planning They incorporate mechanisms to effectively use communication and other interaction mechanisms in their action deliberationEmotionsEBDI ModelEmotion reasoning --- one of the common sense reasoning!26OCC Emotion Model The theory of human emotions and emotional reactions to events proposed by A. Ortony, G. L. Clore, and A. Collins. (1988) (OCC model)Emotion classes, each consisting of several emotions or emotional reactions, for each emotion, eliciting conditions determine under what circumstance the emotion is elicitedWell-beingFortunes-of-othersProspect-basedAttributeCompoundAttraction

7/25/201127Fuhua Lin, SCIS, FST, Athabasca UniversityEmotions According to OCC Emotion theory (Andrew Ortony, Gerald L. Clore, and Allan Collins, 1988), there are several emotion classes, each consisting of several emotions or emotional reactions. For each emotion, eliciting conditions determine under what circumstance the emotion is elicited.1. Well-being emotions are reactions to desirable or undesirable events. For example, if a desirable event occurs, then a joy emotion is elicited, whereas if an undesirable events occurs, then a distress emotions emotion is elicited.2. Fortunes-of-others emotions are reactions to events involving others. They vary according to whether they are desirable or undesirable for the self and whether they are believed to be desirable or undesirable for the other person. For example, if an event occurs that is undesirable for the self and desirable for the other person, then a resentment emotion is elicited.3. Prospect-based emotions are reaction to events that they likely to occur but have not yet occurred and reactions to anticipated events that occur or fail to occur.They vary according to whether they are desirable or undesirable and whether they have not yet occurred, they have occurred, or they have failed to occur. For example, if a desirable event is likely to occur, then a hope emotion is elicited. If an undesirable event has failed to occur, then the relief emotion is elicited.4. Attribute emotions are reactions to actions performed by the self or others of which the self approves or disapproves. For example, if the self performs an action approved by the self, then the pride emotion is elicited; if another person performs an action approved by the self, then the appreciation emotion is elicited.5. Compound emotions are combinations of well-being and attribution emotions. For example, gratitude is a combination of joy and appreciation.6. Attraction emotions are positive and negative reactions to objects. For example, a person likes or dislikes various objects.[1] Andrew Ortony, Gerald L. Clore, and Allan Collins, (1988). The cognitive structure of emotions, Cambridge, UK: Cambridge University Press. 27Formalization of Part of OCC model (Mueller, 2006)Agent sort: a, a1, a2, Belief sort: a, b1, b2, Event sort: e, e1, e2, , and Not(e)Fluent sort: f, f1, f2, Object sort: o, o1, o2, Real number sort: x, x1, x2, 7/25/201128Fuhua Lin, SCIS, FST, Athabasca UniversityFluent --- represent facts (factors) used to specify the electing conditions of emotions

Believe(a, e) --- agent a believes that b has occurredBelieve(a, Not(e) ---- agent a believes that e has not occurredDesirability(a1, a2, e, x): agent a1 believes that the desirability of event e to agent a2 is x, where -1 x 1. a1 and a2 may be the same.Praiseworthiness(a1, a2, e, x): agent a1 believes that the praiseworthiness of event e performed by agent a2 is x, where -1 x 1. a1 and a2 may be the same. Anticipate(a, e, x): agent a anticipates that event e will occur with likelihood x, where 0 x 17/25/201129Fuhua Lin, SCIS, FST, Athabasca UniversityEmotion expression functionsJoy(a, e): agent a is joyful about event eDistress(a, e): agent a is distressed about event e.Happyfor(a1, a2, e): agent a1 is happy for agent a2 regarding event eSorryFor(a1, a2, e): agent a1 is sorry for agent a2 regarding event eResentment(a1, a2, e): agent a1 is resentful of agent a2 regarding event eGloating(a1, a2, e): agent a1 gloats toward agent a2 regarding event e.7/25/201130Fuhua Lin, SCIS, FST, Athabasca University30Learning

What to learnBody language (emotions)Communication patternsDomain knowledge (quiz bank, determine the degree of difficulty, students levels, Game play knowledge

How to learnCentralized learningDistributed learningCase-based learningStudent agents --- how to build student models, learn from human users, pedagogical agents, and other agents, Pedagogical agents --- how to group students, how to generate quizzes, how to provide hints, how to scoreNPCs --- how to learn from humans and their agents?

7/25/201131Fuhua Lin, SCIS, FST, Athabasca UniversityHow to build agents via learning?What to learn and how to learn

31Pedagogical Agents AlgorithmsHow to determine a student 's level and to assign a correct game room; Decide what kinds of peer virtual students will be best for this student, assuming we have a repository of virtual agents (NPCs) available; During the game-play, quiz generation and sequencing, given a group of real players and virtual players. 7/25/201132Fuhua Lin, SCIS, FST, Athabasca UniversityQuestion Item Metadata

7/25/201133Fuhua Lin, SCIS, FST, Athabasca UniversitySimulating Human Communicative Strategies

Simulating Human communicative strategiesCompose explanations spoken or textual; Deliver encouragement, critiques and maintain a mixed initiative dialogue; Analyze a student explanation, spoken or textual; Question students approachRecognize students affect (emotion, focus of attention, or motivation) Engage students in role playing; hire partners for training interactive skills. (Woolf, 2009)

7/25/201134Fuhua Lin, SCIS, FST, Athabasca UniversityOur PublicationsS. Leung, S. Virwaney, F. Lin (2011, Submitted), TSI-enhanced Pedagogical Agents, TESL 2011, Dalian, China.Martin Weng, Fuhua Lin, Timothy K. Shih, Maiga Chang, Ireti Fakinlede, A Conceptual Design of Multi-Agent based Personalized Quiz Game, The 11th IEEE International Conference on Advanced Learning Technologies (ICALT 2011) July 6-8, 2011, Athens, Georgia, USA (accepted) Ning Xia, Fuhua Lin, Aishuang Li, MODELING AND VISUALIZATION OF FRUIT TREES IN HORTICULTURE, in a book "Computers and Education" edited by Sergei Abramovich Blair, Jeanne & F. Lin (2011). An Approach for Integrating 3D Virtual Worlds with Multiagent Systems, ISeRim workshop - IANA 2011 (March, 2011; Singapore) Armstrong, AJ & F. Lin, (2010) Modelling and Personalizing Curriculum with Colored Petri Nets, ICCE 2010 (WIPP). F Lin, Kinshuk, & M Dutchuk, (2009). Multiagent architecture for incorporating adaptivity feature into 3D learning environments, The 6th International Workshop on Mobile and Ubiquitous Learning Environments (MULE 2009), Sept 8-12, 2009, Athabasca University, Canada, pp 33-35, Mark Dutchuk, Khalid Aziz Muhammadi, Fuhua Lin (2009), QuizMASter - A Multi-Agent Game-Style Learning Activity, EduTainment 2009, Aug 2009, Banff, Canada, Learning by Doing, (eds.), M Chang, R. Kuo, Kinshuk, G-D Chen, M. Hirose, LNCS 5670, 263-272. 7/25/201135Fuhua Lin, SCIS, FST, Athabasca University