In this work we hypothesize that giving students a reflective reading after spoken dialog tutoring in qualitative physics will improve learning. The reading is designed to help students compare similar aspects of previously tutored problems, and to abstract their commonalities. We also hypothesize that student motivation will affect how well the text is processed, and so influence learning. We find that the beneficial effects of the reflective text significantly interact with motivation, such that moderately motivated students learn significantly more from the reflective text than from a non-reflective control text. More poorly or highly motivated students did not benefit from reflective text. These results demonstrate that implicit reflection can improve learning after dialog tutoring with a qualitative physics tutor. They further demonstrate that this result can be obtained with a reflective/abstractive text without recourse to dialog, and that the effectiveness of the text is sensitive to the motivation level of the student.

We used a stimulus and response method in cognition to consider agents as situated in their specific (Binti Abdullah et al, 2010) to uncover correlation patterns context as it was realized that people are strongly affected of the physical artefact-communication during specific by, and possibly dependent on their environment contexts of communications. We found preliminary empirical (Susi & Ziemke, 2001). With this shift of focus, new interactive evidence that the physical artefacts influence the theories of cognition have emerged. These interactive communication process in a mutually constraining relationship theories such as situated cognition (Clancey, 1997), with the contexts. In which the context is made up and distributed cognition (Hutchins, 1999), are noted for of the teams' practice that includes how they collaborate, their emphasis on the relationship between cognition, and the physical setting, situations, and participation role.

If given manually-crafted goal selection knowledge, goal reasoning agents can dynamically determine which goals they should achieve in complex environments. These agents should instead learn goal selection knowledge through expert interaction. We describe T-ARTUE, a goal reasoning agent that performs case-based active and interactive learning to discover goal selection knowledge. We also report tests of its performance in a complex environment. We found that, under some conditions, T-ARTUE can quickly learn goal selection knowledge.

In this paper we introduce our recommender Happy Movie, a Facebook application for movie recommendation to groups. This system exploits information about the social relationships and behaviour of the users to provide better recommendations. Our previous works have shown that social factors improve the recommendation results. However it required many questionnaires to be filled for obtaining the social information, so we have moved to a social network environment where this information is easily available.

What determines difficulty of solving a problem? Although this question has been studied before, we found examples which show large differences in problem difficulty which are not explained by concepts identified in previous research. This differences are caused mainly by the structure of a problems' state spaces and cannot be easily captured by static metrics like size of the state space or the length of a solution. To address these unexplained differences, we propose a computational model of human problem solving behaviour. We provide evaluation of the model over large scale dataset (hundreds of hours of problem solving, more than 100 problem instances) for three transport puzzles (Sokoban, Rush hour, and Replacement puzzle).

We address a scheduling problem in the context of military aircraft maintenance where the goal is to meet the aircraft requirements for a number of missions in the presence of breakdowns. The assignment of aircraft to a mission must consider the requirements for the mission, the probability of aircraft failure, and capacity of the repair shop that maintains the aircraft. Therefore, a solution both assigns aircraft to missions and schedules the repair shop to meet the assignments. We propose a dispatching heuristic algorithm; three complete approaches based on mixed integer programming, constraint programming, and logic-based Benders decomposition; and a hybrid heuristic-complete approach. Experiments demonstrate that the logic-based Benders variation combining mixed integer programming and constraint programming outperforms the other approaches, that the dispatching heuristic can feasibly schedule the repair shop in a very short time, and that using the dispatching solution as a bound marginally improves the complete approaches.

Engineering complete planning domain descriptions is often very costly because of human error or lack of domain knowl- edge. Learning complete domain descriptions is also very challenging because many features are irrelevant to achieving the goals and data may be scarce. We present a planner and agent that respectively plan and act in incomplete domains by i) synthesizing plans to avoid execution failure due to ignorance of the domain model, and ii) passively learning about the domain model during execution to improve later re-planning attempts. Our planner DeFault is the first to reason about a domain’s incompleteness to avoid potential plan failure. DeFault computes failure explanations for each action and state in the plan and counts the number of interpretations of the incomplete domain where failure will occur. We show that DeFault performs best by counting prime implicants (failure diagnoses) rather than propositional models. Our agent Goalie learns about the preconditions and effects of incompletely-specified actions while monitoring its state and, in conjunction with DeFault plan failure explanations, can diagnose past and future action failures. We show that by reasoning about incompleteness (as opposed to ignoring it) Goalie fails and re-plans less and executes fewer actions.

When people interact with language-producing agents (other people or computers), they assume that the shared experience leads to shared representations — of the world, the interaction, and the language used in the interaction. This phenomenon occurs even during interaction with systems that give no evidence of building shared representations. The absence of shared representations leads to errors and delays; alternatively, even simple shared representations can lead to reduced error rates and more efficient interaction. In this talk, we present three case studies: a mobile local business search application that builds no interaction representations; a telephone-based recommendation and review system that builds limited representations of the shared language in the interaction; and computer models of coreference that use shared representations to permit both coreference resolution and referring expression generation. We lay out a range of possibilities for shared representations, show that they can be built incrementally as an interaction progresses, and point to possibilities for future work in probabilistic shared representations for interactive systems.

We propose and evaluate five related algorithms that automatically derive limited-size noun vocabularies from text documents of 2,000-30,000 words.The proposed algorithms combine Personalized Page Rank and principles of information maximization, and are applied to the WordNet graph for nouns. For the best-performing algorithm the difference between automatically generated reduced noun lexicons and those created by human writers is approximately 1-2 WordNet edges per lexical item. Our results also indicate the importance of performing word-sense disambiguation with sentence-level context information at the earliest stage of analysis.

A collection of landmarks is complete if the cost of a minimum-cost hitting set equals h + and there is a minimum-cost hitting set that is an optimal relaxed plan. We present an algorithm for generating a complete collection of landmarks and we show that this algorithm can be extended into effective polytime heuristics for optimal and satisficing planning. The new admissible heuristics are compared with current state-of-the-art heuristics for optimal planning on benchmark problems from the IPC.