Name: Nader Hanna Student ID: 42259541 Supervisor: Prof. Deborah Richards

1. ITEC810 End of Semester Workshop- 2012 Multi-user Virtual Environment to aid Collaborative Learning and Transfer of Scientific Knowledge and Inquiry Skills Name: Nader Hanna Student ID: 42259541 Supervisor: Prof. Deborah Richards

2. Agenda • Introduction • What is Activity Theory? • The Aim of the Study • The Focus of the Study • Literature Review about on Using AT in Designing Collaborative environment • Literature Review About the Approaches of Agent Collaboration. • Agent-to-agent Collaboration • One-way Human-agent Collaboration • Two-way Human Agent Collaboration • Proposed Framework of Multi-agent Collaborative Virtual Learning (MACVILLE) • Proposed Collaborative Agent Architecture • Challenges • Conclusion

3. Introduction • Why collaborative learning? Classroom learning improves significantly when students participate in collaborative learning [Johnson and Johnson, 1999]. • Why virtual environment? Using virtual environment increases the involvement of the students in learning activity. • What is Intelligent agent? A software agent that has some intelligence and autonomous in its environment , it takes decisions by itself. • What is BDI agent reasoning model? Belief-Desire-Intention model, agent has knowledge about his environment, plans to achieve and methods to achieve these plans

4. Tools Outcome Subject Object Community Rules Division of Effort What is Activity Theory? • Activity Theory (AT) is a theoretical framework for analyzing human practices in a given context. It is used to arrange the collaborative learning. • Subject: Members of the learning group • Object: Experiences, Knowledge , Products,… • Community: learning group, system, society ,… • Tools: plans, spoken language, ICT applications, devices ,… • Rules: practices, collaboration, negotiation ,… • division of labor:learners, tutors, supervisors, virtual agents • Subject uses Tools to interact with Object. • Community uses Division of Effort to interact with Object, and uses Rules to interact with Subject. Structure of Activity theory

5. The aim of the study • The aim of this study is to design, implement, and test a collaborative multi-agent multi-user virtual learning environment. • The collaborative environment will include 3 levels of collaboration: • Human-human collaboration in physical world. • Human-agent collaboration in virtual world. • Human-human collaboration in virtual world.

6. The virtual environment

7. Levels of collaboration Human-agent virtual collaboration 2 Human-human physical collaboration 1

8. Levels of collaboration (cont.) 3 Human-human virtual collaboration

9. The Focus of the Study Activity Theory Collaborative Learning Agent Reasoning Model Learning Scientific Conclusion We target this part

10. Literature Review on Using AT in Designing Collaborative Environment • Miao [1] presents a conceptual framework for the design of virtual problem-based learning environments in the light of activity theory. • Gifford and Enyedy [2] propose a framework called Activity Centered Design (ACD). Their proposed framework is based on three main concepts of AT. • Liang et al. [3] further build on the six steps to define components and their relationships for collaborative network learning. • Norris and Wong [4] use AT to identify any difficulties that users may have when navigating through QuickTime Virtual Reality Environments (QTVR). • Zurita and Nussbaum [5] identified six steps to propose a conceptual framework for mobile Computer Supported Collaborative Learning (MCSCL) activities.

11. Literature Review on the approaches of agent collaboration • We determined 3 possible categories of agent collaboration: • Agent-agent Collaboration…interacting agents in a dynamic environment using techniques such as Tuple-Spaces, Group Computation and Roles. • One-way Human-agent Collaboration…where agent behave as a mediator or facilitator to human-human collaborative activities. • Two-way Human-agent Collaboration…where agent directly interacts with human actions.

12. Agent-to-agent collaboration • use of a shared/global model between agents to achieve the determined goal. • Agent Collaboration Approaches: • Tuple-spaces provide a multi-agent-like architecture, where agents can collaborate through writing, reading or removing tuples in the space. • Group Computation a way to program group based activities . • Activity Theory is a framework to design the mediated interaction that may happen between agents. • Roles are used to define common interactions between agents in virtual environments.

13. One-way human-agent collaboration • actions performed by the human with assistance from the agent. • Aguilar et al. [6] Agent could assist the group during the execution stage of a Team Training Strategy • Yacine and Tahar [7] the role of the agent could be a mediator where the agent’s role is to facilitate the collaboration between users and give feedback • Zhang et al. [8, 9]The role of the mediator agent could be to facilitate the communication of a user with other users • Luin et al. [10] the agent could collaborate with the user by answering the user’s questions while navigating into a virtual world

14. Two-way human agent collaboration • the actions of both the human and agent interleave and depend on each other. • Miller et al [11] Humans and/or agents could be are working together to achieve a goal • Lesh et al. [12] plan recognition algorithm in order to reduce communication during collaboration between a human and an agent • Miao et al. [13] Agent could behave and modify his behaviour according to human actions, for example to train learners to handle abnormal situations while driving cars • Hedfi et al. [14] Collaboration between human and agent could take the form of negotiation in design something • Fan and Yen [15] To make a teamwork between human and agent, agent may need estimate its human partner’s cognitive load

15. Virtual Collaborative Learner Companion Agent Target Determining Communication Network Protocol Text Voice Database Awareness Learner Colleague 3D character graphic 3D character animation Collaborative Virtual World Roles Specifying Tools Collaboration Discussion Guidance Collect evidences Conclusion Making Outcome Write report Social Interaction Negotiation Decision-making Subject Object Roleplaying Agent reasoning model Progression • BDI • Co-Operation • Social KnowledgeSharing Virtual communication • Interaction • Guidance NoteTaking Community Rules Division of Effort Conclusion Making Decision Justifying Face-to-face communication • Interaction • Guidance • Discussion • Role taking Idea Defending Questioning Learner-agent group Online communication Comparing Role playing • Debating • Negotiating Conclusion Making Learner-learner physical group Decision Updating Learner-learner online group Proposed framework of Multi-agent collaborative virtual learning (MACVILLE)

16. Social and Collaborative Processes The Intelligent and Cognitive processes Situation Understanding Situation Memory and its Processes Cognitive Processes Learner Realizing Information Retrieval Situation Understanding Goal Setting Partner Progress Evaluating Knowledge/ Memory Planning Action and Communication PlanExecuting/Adopting Knowledge Integration Learning Proposed collaborative agent architecture

17. Memory and its Processes Information Retrieval Knowledge/ Memory Knowledge Integration The Intelligent and Cognitive processes • The memory or the knowledge base is where the agents store information, knowledge and experience. • two processes related to the memory: Knowledge Integration to add a new experience to the stored knowledge, and Information Retrieval to get the appropriate piece of information

18. Cognitive Processes Situation Understanding Goal Setting Planning Plan Executing/Adopting Learning The Intelligent and Cognitive processes (cont.) • Cognitive processes gives the agent the ability to perform the tasks and change in its environment. • Situation Understanding…using inputs from the environment to understand where is the agent? • Goal Setting…using inputs from the environment and the human learner to determine what the agent going to collaborate with the user. • Planning…the ability to have a different plans for each task. • Executing/Adopting…adopt the plans to perform the task according to the changes in the environment. • Learning…new rules may be added to knowledge base for future usage.

19. Social and Collaborative Processes Social and Collaborative Processes • Social and Collaborative Processes… • Situation Understanding…understanding what is the social situation the agent going to handle. • Learner Realizing…understanding who is the human team member, and what is the role of human in the collaboration activity. • Partner Progress Evaluating…working in a teamwork needs the members to feel the need to others help. • Action and Communication …instructions exchanged between human and agent. Situation Understanding Learner Realizing Partner Progress Evaluating Action and Communication

20. Challenges • Conceptual and design challenges may include: • Determining the factors that control human collaboration and determine successful teamwork. • The difficulties in capturing and measuring the levels and nature of collaboration that take place between human and agent. • Implementation challenges may include: • Creating, selecting or adapting an appropriate implementation framework to extend the [BDI] reasoning model of the agent. • Integrating the framework of agent reasoning with a 3D game engine already in use (unity3D).

21. Conclusion • There are different approaches for agent collaboration in 3D virtual environment. • Activity Theory is a framework used to manage collaborative learning in physical classroom, and could be use in virtual collaborative environment . • Two-way human-agent collaboration needs the agent to be fully aware of the environment, the task to be achieved, the plan to be done and the partner. • BDI model of agent reasoning give the agent the ability to think in doing required task regardless to participation from other actor. • Collaboration between human user and agent needs the agent to go beyond BDI model.

22. References • Miao, Y.: An Activity Theoretical Approach to a Virtual Problem Based Learning Environment. In: the 2000 International Conference on Information in the 21 Century: Emerging Technologies and New Challenges, pp. 647-654. (2000) • Gifford, B.R., Enyedy, N.D.: Activity Centered Design: Towards a Theoretical Framework for CSCL. In: Roschelle, C.H.J. (ed.) Proceedings of the 1999 conference on Computer support for collaborative learning, pp. 22-37. International Society of the Learning Sciences, Palo Alto, California (1999) • Liang, X., Wang, R., Bai, G.: A Multi-Agent System Based on Activity Theory for Collaborative Network Learning. In: First International Workshop on Education Technology and Computer Science (ETCS '09), pp. 392-397. (2009) • Norris, B.E., Wong, B.L.W.: Activity Breakdowns in QuickTime Virtual Reality Environments. Proceedings of the First Australasian User Interface Conference (AUIC '00), pp. 67 - 72. IEEE Computer Society, Canberra, ACT (2000) • Zurita, G., Nussbaum, M.: A Conceptual Framework Based on Activity Theory for Mobile CSCL. British Journal of Educational Technology 38,211-235 (2007)

23. References (cont.) • Aguilar, R.A., Antonio, A.d., Imbert, R.: An Intelligent Collaborative Virtual Environment for Team Training -- A Preliminary Report. In: 15th International Conference on Computing (CIC '06), pp. 236-239. (2006) • Yacine, L., Tahar, B.: Supporting Collaboration in Agent-Based Collaborative Learning System (SACA ). In: Information and Communication Technologies ( ICTTA '06 ), pp. 2843-2848. (2006) • Zhang, C., Xi, J., Yang, X.: An Architecture for Intelligent Collaborative Systems Based on Multi-agent In: 12th International Conference on Computer Supported Cooperative Work in Design (CSCWD '08), pp. 367 - 372. (2008) • Zhang, P., Li, X.: The Framework of Multi Intelligent Agent Based on Collaborative Design. In: International Conference on Future BioMedical Information Engineering ( FBIE '09), pp. 513 - 517. (2009) • Luin, J.v., Akker, R.o.d., Nijholt, A.: A Dialogue Agent for Navigation Support in Virtual Reality. In: extended abstracts on Conference on Human Factors in Computing Systems (CHI '01), pp. 117-118. ACM, (2001)

24. References (cont.) • Miller, M.S., Yin, J., Volz, R.A., Ioerger, T.R., Yen, J.: Training Teams with Collaborative Agents. In: Proceedings of the 5th International Conference on Intelligent Tutoring Systems, pp. 63-72. Springer-Verlag, 745826 (2000) • Lesh, N., Rich, C., Sidner, C.L.: Using plan recognition in human-computer collaboration. In: Proceedings of the seventh international conference on User modeling, pp. 23-32. Springer-Verlag New York, Inc., 317331 (1999) • Miao, Y., Hoppe, U., Pinkwart, N.: Naughty Agents Can Be Helpful: Training Drivers to Handle DangerousSituations in Virtual Reality. In: Sixth International Conference on Advanced Learning Technologies (ICALT '06), pp. 735-739. (2006) • Hedfi, R., Ito, T., Fujita, K.: Towards Collective Collaborative Design: An Implementation of Agent-Mediated Collaborative 3D Products Design System. In: 2010 International Symposium on Collaborative Technologies and Systems (CTS), pp. 314-321. (2010) • Fan, X., Yen, J.: Realistic cognitive load modeling for enhancing shared mental models in human-agent collaboration. In: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, pp. 1-8. ACM, 1329197 (2007)