Ontology-Based Intelligent Agents in Workplace eLearning

Despite the ever increasing practices of e-learning, most workplace e-learning applications fail to meet the learners’ needs and ultimately fail to serve the organization’s quest for success. The dominance of technology-oriented approaches makes e-learning applications less goal-effective, and makes them perceived to be of poor quality and design. To solve this problem, a performance oriented approach is presented in this study. This approach aims to align the individual learning needs vis-à-vis the organizational goals and makes learning connected with work performance. Based on the approach, a prototype system has been developed that uses intelligent agent and ontology technology. A set of experiments have been conducted to demonstrate the effectiveness of the approach

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Goal-oriented Dialogue Policy Learning from Failures

Reinforcement learning methods have been used for learning dialogue policies. However, learning an effective dialogue policy frequently requires prohibitively many conversations. This is partly because of the sparse rewards in dialogues, and the very few successful dialogues in early learning phase. Hindsight experience replay (HER) enables learning from failures, but the vanilla HER is inapplicable to dialogue learning due to the implicit goals. In this work, we develop two complex HER methods providing different trade-offs between complexity and performance, and, for the first time, enabled HER-based dialogue policy learning. Experiments using a realistic user simulator show that our HER methods perform better than existing experience replay methods (as applied to deep Q-networks) in learning rate