We have developed computer environments that support learning by teaching and the use of self regulated learning (SRL) skills through interactions with virtual agents. More specifically, students teach a computer agent, Betty, and can monitor her progress by asking her questions and getting her to take quizzes. The system provides SRL support via dialog-embedded prompts by Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning in complex science domains and facilitate development of metacognitive skills. More recently, we have also employed sequence analysis schemes and hidden Markov model (HMM) methods for assigning context to and deriving aggregated student behavior sequences from activity data. These techniques allow us to go beyond analyses of individual behaviors, instead examining how these behaviors cohere in larger patterns. We discuss the information derived from these models, and draw inferences on students’ use of self-regulated learning strategies.

The goal of this paper is to present four key assumptions regarding the measurement of cognitive and metacognitive regulatory processes used during learning with hypermedia. First, we assume it is possible to detect, trace, model, and foster SRL processes during learning with hypermedia. Second, understanding the complex nature of the regulatory processes during learning with hypermedia is critical in determining why certain processes are used throughout a learning task. Third, it is assumed that the use of SRL processes can dynamically change over time and that they are cyclical in nature (influenced by internal and external conditions and feedback mechanisms). Fourth, capturing, identifying, and classifying SRL processes used during learning with hypermedia is a rather challenging task.

LODE is a web tool for children that are novice readers, and is primarily meant for deaf children. It proposes written stories and interactive games for reasoning, globally, on the stories. In this paper, first, we motivate the rationale of LODE, and explain its reasoning games. Then we briefly describe the design of the web client-server architecture of LODE; the server employs a constraint programming system for creating and solving the LODE games in real time. Finally, we concentrate on two evaluations of the latest prototype of LODE: one with hearing novice readers; another one with deaf readers. We conclude by discussing the results of the evaluations, and their implications for LODE.

Unfortunately, many students stop using these beneficial learning practices as soon Presented by Stephen Grossberg, Department of Cognitive as the metatutoring ceases. Apparently, the metatutors were and Neural Systems, Center for Adaptive Systems, and Center nagging rather than convincing. This talk will present a of Excellence for Learning in Education, Science, and study of Pyrenees, a metatutor that coaches students to focus Technology, Boston University, Boston, MA 02215 on learning domain principles rather than solutions to A deep and rational understanding of the factors that influence examples. It was convincing, in that students who were effective education and learning technologies depends taught probability with Pyrenees used principle-based problem on a corresponding understanding of how the brain in health solving on post-test more so than students taught by Andes, and disease controls learned behaviors. There has been a which did not focus students on principles. Moreover, revolution in discovering new computational paradigms, organizational when all students were transferred to Andes for learning principles, mechanisms, and models of how of physics, those who were metatutored used the principlefocused learning processes enable brains to give rise to minds.

As machines become more intelligent and take on more responsibilities, their decision-making capabilities must be informed and constrained by a coherent, integrated moral/ethical structure with no internal inconsistencies for everyone’s safety and well-being. Unfortunately, no such structure is currently agreed upon to exist. We propose to solve this problem by a) drawing upon experimental evidence and lessons learned from evolution and economics to show that morality is actually objective and derivable from first principles; b) presenting a coherent, integrated, platonic ethical system with no internal inconsistencies that flows naturally from a single high-level logically-derived Kantian imperative to low-level reflexive "rules of thumb" that match current human sensibilities; and c) suggesting a biologically-inspired architecture which supports and enforces this system which can be relatively easily implemented.

Insufficient knowledge and resources is not only a biological constraint on human and animal intelligence, but also has important functional implications for artificial intelligence (AI) systems. Traditional theories dominating AI research typically assume some kind of sufficiency of knowledge and resources, so cannot solve many problems in the field. AI needs new theories obeying this constraint, which cannot be obtained by minor revisions or extensions of the traditional theories. The practice of NARS, an AI project, shows that such new theories are feasible and promising in providing a new theoretical foundation for AI.

The field of Artificial Intelligence has, for a long time, neglected the role of emotions in human cognition, with few but notable exceptions. This has been motivated in part by the assumption that the emulation of human rationality by a machine is sufficient for attaining general human-level intelligence. This paper reviews neuroscientific results showing empirical evidence, consistently for over a decade, sustaining that emotion mechanisms in the brain play a fundamental role in decision making processes, as well as in cognitive regulation. Moreover, this role takes place regardless of whether the subject is aware of any emotion. These mechanisms are particularly important in social contexts. Lesions in the pathways supporting these mechanisms provoke serious impairments on social behavior. For instance, subjects with lesions in the pathways between the orbitofrontal cortex and the amygdala are no longer able to sustain an healthy social live, despite their intact intellectual capabilities. Strikingly, these patients are even able to verbally describe what would be the proper social behavior, although are unable to follow it. One important mechanism in social contexts is empathy, fundamental for proper social relations. It has been proposed that empathy is founded on mechanisms analogous to the mirror neurons.

The development of artificial intelligence systems has to date been largely one of manual labor. This Constructionist approach to A.I. has resulted in a diverse set of isolated solutions to relatively small problems. Small success stories of putting these pieces together in robotics, for example, has made people optimistic that continuing on this path would lead to artificial general intelligence. This is unlikely. "The A.I. problem" has been divided up without much guidance from science or theory, resulting in a fragmentation of the research community and a set of grossly incompatible approaches. Standard software development methods come with serious limitations in scaling; in A.I. the Constructionist approach results in systems with limited domain application and severe performance brittleness. Genuine integration, as required for general intelligence, is therefore practically and theoretically precluded. Yet going beyond current A.I. systems requires significantly more complex integration than attempted to date, especially regarding transversal functions such as attention and learning. The only way to address the challenge is replacing top-down architectural design as a major development methodology with methods focusing on self-generated code and self-organizing architectures. I call this Constructivist A.I., in reference to the self-constructive principles on which it must be based. Methodologies employed for Constructivist A.I. will be very different from today's software development methods. In this paper I describe the argument in detail and examine some of the implications of this impending paradigm shift.

One of the open issues in developing large-scale computational models of the brain is how the transfer of information between communicating cortical regions is controlled. Here, we present a model where the basal ganglia implement such a conditional information routing system. The basal ganglia are a set of subcortical nuclei that play a central role in cognition. Like a switchboard, the model basal ganglia direct the communication between cortical regions by alerting the destination regions to the presence of important signals coming from the source regions. This way, they can impose serial control on the massive parallel communication between cortical areas. The model also incorporates a possible mechanism by which subsequent transfers of information control the release of dopamine. This signal is used to produce novel stimulus-response associations by internalizing the representation being transferred in the striatum. We discuss how this neural circuit can be seen as a biological implementation of a production system. This comparison highlights the basal ganglia as bridge between computational models of small-size brain circuits and high-level characterizations of complex cognition, such as cognitive architectures.

In this paper, we present a cognitive model of frequency-based decision-making applied to the task of landmine detection. The model is implemented in the ACT-R cognitive architecture and is strongly constrained by the cognitive primitives of the architecture. We then generalize the model to another task in the domain of macroeconomic decision-making using the same architecture, pursuing theoretical parsimony. We describe each model's representation requirements, assess their fits to the data, and analyze their performance scaling as a function of task and architectural parameters. Efforts to generalize the landmine detection model to macroeconomic decision making showed that reasonable fits to the macro-economic performance data could be achieved by models based either on procedural knowledge or declarative knowledge. This finding underscores the importance of distinguishing between processing strategies employed to execute tasks. Such detail appears needed to understand the neural foundations of frequency-based decision-making.