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In this paper, we address the problem of suboptimal behavior during online partially observable Markov decision process (POMDP) planning caused by time constraints on planning. Taking inspiration from the related field of reinforcement learning (RL), our solution is to shape the agent’s reward function in order to lead the agent to large future rewards(More)
Prior research has established that active participation and collaboration by students results in multiple benefits during wiki-based CSCL activities. However, achieving such behavior can be a challenge without external motivation. To increase active participation and collaboration by users, we developed an enhanced wiki called the Written Agora. Using(More)
— Recent years have seen a surge in the use of intelligent computer-supported collaborative learning (CSCL) tools for improving student learning in traditional classrooms. However, adopting such a CSCL tool in a classroom still requires the teacher to develop (or decide on which to adopt) the CSCL tool and the CSCL script, and design the relevant(More)
We address the problem of suboptimal behavior caused by short horizons during online POMDP planning. Our solution extends potential-based reward shaping from the related field of reinforcement learning to online POMDP planning in order to improve planning without increasing the planning horizon. In our extension, information about the quality of belief(More)
In many real-world applications of multi-agent systems, agent reasoning suffers from bounded rationality caused by both limited resources and limited knowledge. When agent sensing to overcome its knowledge limitations also requires resource use, the agent’s knowledge refinement is affected due to its inability to always sense when and as accurately as(More)