It is still unclear what metacognition is. Two main theories about metacognition are reviewed, each of which claims to provide a better explanation of the phenomenon, while discrediting the other theory as inappropriate. My claim is that in order to do justice to the complex phenomenon of metacognition, we must distinguish two levels of this capacity. It can be shown that each of these theories has been trying to explain only one of the two levels and that, consequently, the conflict between them can be dissolved. Finally, I characterize each level and explain some of their interactions.

Mixed reality is an important classroom tool for managing complexity from both the students' and instructor's standpoints. It can be used to provide important scaffolds when introducing robotics, by allowing elements of perception and control to be abstracted, and these abstractions removed as a course progresses (or left in place to introduce robotics to younger groups of students). In prior work, we have illustrated the potential of this approach both in providing scaffolding, building an inexpensive robotics laboratory, and also providing control of evaluation of robotics environments for student evaluation and scientific experimentation. In this paper, we explore integrating extensions and improvements to the mixed reality components themselves as part of a course in applied artificial intelligence and robotics. We present a set of assignments that in addition to exploring robotics concepts, actively integrate creating or improving mixed reality components. We find that this approach better leverages the advantages brought about by mixed reality in terms of student motivation, and also provides some very useful software engineering experience to the students.

Over the years, competitions have been important catalysts for progress in Artificial Intelligence. We describe one such competition, the Trading Agent Competition for Supply Chain Management (TAC SCM). We discuss its significance in the context of today’s global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past six years. TAC SCM requires autonomous supply chain entities, modeled as agents, to coordinate their internal operations while concurrently trading in multiple dynamic and highly competitive markets. Since its introduction in 2003, the competition has attracted over 150 entries and brought together researchers from AI and beyond in the form of 75 competing teams from 25 different countries.

As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs that the current Twitter interface fails to support, and argue for better representations of content for solving these challenges. We present a scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.

We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece in September 2009. We give an overview of how the competition has developed since its first run in 2005, and its relationship with the AI planning field. This run of the competition focused on translators that when input with some formal description in an application-area-specific language, output solver-ready domain models. Despite a fairly narrow focus within knowledge engineering, seven teams took part in what turned out to be a very interesting and successful competition.

Standard approachs to belief change assume that the underlying logic contains classical propositional logic. Recently there has been interest in investigating approaches to belief change, specifically contraction, in which the underlying logic is not as expressive as full propositional logic. In this paper we consider approaches to belief contraction in Horn knowledge bases. We develop two broad approaches for Horn contraction, corresponding to the two major approaches in belief change, based on Horn belief sets and Horn belief bases. We argue that previous approaches, which have taken Horn remainder sets as a starting point, have undesirable properties, and moreover that not all desirable Horn contraction functions are captured by these approaches. This is shown in part by examining model-theoretic considerations involving Horn contraction. For Horn belief set contraction, we develop an account based in terms of weak remainder sets. Maxichoice and partial meet Horn contraction is specified, along with a consideration of package contraction. Following this we consider Horn belief base contraction, in which the underlying knowledge base is not necessarily closed under the Horn consequence relation. Again, approaches to maxichoice and partial meet belief set contraction are developed. In all cases, constructions of the specific operators and sets of postulates are provided, and representation results are obtained. As well, we show that problems arising with earlier work are resolved by these approaches.

In discrete dynamical systems change results from actions. As such, given a set of observations, diagnoses often take the form of posited events that result in the observed behaviour. In this paper we revisit formal characterizations of diagnosis, and their relationship to planning. We do so from both a theoretical and a computational perspective. In particular, we extend the characterization of diagnosis to deal with the case of incomplete information, and rich preferences. We also explore the use of state-of-the-art planning technology for the automated generation of diagnoses. Examining several classes of diagnosis problems, we provide both proof of concept and benchmark experiments, the latter showing superior performance to a leading diagnosis engine. Our findings help support the hypothesis that planning technology holds great promise for efficient generation of diagnoses.

We report (to our knowledge) the first evaluation of Constraint Satisfaction as a computational framework for solving closest string problems. We show that careful consideration of symbol occurrences can provide search heuristics that provide several orders of magnitude speedup at and above the optimal distance. We also report (to our knowledge) the first analysis and evaluation -- using any technique -- of the computational difficulties involved in the identification of all closest strings for a given input set. We describe algorithms for web-scale distributed solution of closest string problems, both purely based on AI backtrack search and also hybrid numeric-AI methods.

Time perception and inferences there from are of critical importance to many autonomous agents. But time is not perceived directly by any sensory organ. We argue that time is constructed by cognitive processes. Here we present a model for time perception that concentrates on succession and duration, and that generates these concepts and others, such as continuity, immediate present duration, and lengths of time. These concepts are grounded through the perceptual process itself. The LIDA cognitive model is used to illustrate these ideas.

For the past several years, we have been using robots in our introductory computer science course. Although this has been challenging for many reasons, it has also been very rewarding on a number of fronts, both for the students and for us. However, in order for this to occur, we had to adapt to what we perceived as “chaotic code.” In this paper we describe lessons learned by watching what the students do, where they have trouble, and what they enjoy. Further, we discuss what the implications of focusing on creativity has had on teaching and assessment.