Abstract: Pathfinding involves solving a planning problem with agents seeking optimal paths from a start state to a goal state. The pathfinding process involves utilizing the full state space information available to agents tofind the least expensive route to the goal. However most solutions to the pathfinding problem have solely focused on using graph based terrain representations and graph search algorithms to obtain a path. This paper replaces part of the search with a direct geometrical solution. We provide some preliminary indication of the potential merit of the approach.

Abstract: We provide a general definition of mixed-initiative for application to the area of computer games. This definition is used to provide a survey of the ways in which mixed- initiative has been applied to games up to this point, and where and how mixed-initiative could be applied to improve the quality of games in the future. The need for algorithmic content creation though mixed-initiative interactions is demonstrated, and the application of mixed-initiative interactions to the challenge of developing realistic interactions with computer-controlled agents in an open- ended role-playing game is analyzed.

Abstract: Successful mixed initiative systems employ mechanisms that explicitly recognize opportunities for initiatives among the system and the users. In this paper, we propose a theory based framework, founded on the principles of Self Regulated Learning, that recognizes strategies and tactics learners used in their interactions. These interactions are observed from within gStudy, a learning tool that students use as part of learning activity. Production rules encode SRL-specific knowledge in an OWL-based formal ontology and JESS is used as an inference engine to recognize strategies and tactics used by learners in specific reading and problem-solving activities. Using such inferences we demonstrate how the system recognizes opportunities for mixed-initiative interactions to guide learners who veer away from optimal SRL strategies.

Abstract: The mechanism of Self-Regulated Learning (SRL) is a complex interactive process involving cognitive selfregulation. Self-regulation is enacted by learners when they form specific learning strategies that they actively or subconsciously adopt in order to enhance their learning experience. Our research concerns the development of a framework that allows for Mixed Initiative Interactions (MII) to help the learners optimize their learning strategies and help them self regulate better. We represent the underlying SRL tactics and strategies in an ontology. We utilize production rules to translate and disseminate SRL tactics and strategies represented in the ontology. This paper focuses on the design and development of the production rules. We describe how the production rules are employed in the context of a mixed-initiative system named MIEDNA.

Abstract: Online help technologies range from sophisticated graphical interfaces that guide users, to proactive and intelligent tutorial interactions. Introducing ready, able, and willing human helpers in help scenarios has proven to be an important milestone in help technologies. In this paper we argue how techniques of mixed-initiative interactions can be successfully deployed in online help. We contend that a well-defined context, that encapsulates the relative knowledge, preferences, and task goals of the helper and the helpee, is integral to the success of mixed-initiative help interactions. We present empirical results to highlight the need for context-awareness in help scenarios and argue how such contexts dynamically regulate the contributions of the conversants, the helper, the helpee, and the help system, in mixed-initiative interactions.