Socializing Pedagogical Agents for Personalization in Virtual Learning Environments Maryam Ashoori, Chunyan Miao, Yundong Cai School of Computer Engineering Nanyang Technological University Singapore 639798 {Y050022, ascymiao, CAIY0004}@ntu.edu.sg Abstract Personalization in virtual learning environments is the system ability to provide individualization and a set of per- sonalized services such as personalized content manage- ment, learner model, or adaptive instant interaction. The intelligent agent technology has potential regarding the creation of such personalized, adaptive and interactive e- learning applications. However, most of the available solu- tions have so far focused on porting existing courses with traditional teaching methods onto the virtual environments, making them available in an attractive animated interface without any fine-tuning and adaptation to the learner needs. This paper proposes a novel market-inspired collabora- tion model where the agents are self-interested autonomic elements collaborate to achieve a comprehensive learner model. Mentor agent makes decisions on top of a Dempster- Shafer belief accumulation to help student whenever she believes student has lost the clues and needs help. Pro- posed architecture is validated by applying on a sample agent augmented virtual environment designed to engage and motivate students at the lower secondary level in Sin- gapore. Extensive experiments illustrate the effectiveness of the proposed interaction model where students have found the mentor agent as believable as a virtual teacher. 1. Introduction An emerging issue in pedagogy is adapting the teaching to the needs of various learners. Pedagogical agents have the potential ability to effectively address individualization. During last decade, lots of empirical researches have been done into pedagogical agents, their effectiveness, or limi- tations. STEVE [1], MERLIN [2], Herman the Bug, and COSMO [3] are some of them. These agents can engage in a continuous dialogue with the student, and emulate as- pects of dialogue between a human teacher and student in instructional settings. This paper is toward an effective ne- gotiation protocol among self interested mentors of a virtual learning environment in order to achieve personalized learn- ing process. The idea, however, is not limited to e-learning systems and can be applied to a wide variety of application domains where personalizing the content can improve the quality of experience. The agent augmented virtual world used for prototype validation in this paper is a Singapore version of the exist- ing US City environment developed by Harvard University in which the synthetic characters in the new virtual environ- ment are augmented with advanced agent technologies in order to investigate contextual, situational, social, and emo- tional dimensions of virtual experiences for learning. Singa- pore River City(SRC) is a graphical multi-user virtual envi- ronment with deep content and challenging activities where as the sample story, intelligent agents help students to in- vestigate an unknown disease in the society. This paper is to develop a believable virtual mentor who collaborate with other guide agents of the environment to provide effective personalized interaction with students. 2. Toward An Affective Economy of Pedagogi- cal Agents A general agent called Mentor is defined through the sys- tem which acts as a virtual teacher in environment and con- trols the entire student’s progress and activities. Mentor in- teracts with all the other agents, Guide Agents, evaluates their efficiency and guides the student in all the locations as a permanent virtual mentor. An overall view of this process is illustrated in Figure 1. Once the student leaves a scene, Mentor updates his knowledge model according to the feedback reported by the Guides on that location. Guides are other virtual agents like Doctor, Nurse, and Sick Coolie whom students communi- cate with inorder to investigate the information/knowledge of the environment. Proposed approach is discussed in three following sections of (1) “Learner modeling”’, which talks 1