Researchers have studied problems in metacognition both in computers and in humans. In response some have implemented models of cognition and metacognitive activity in various architectures to test and better define specific theories of metacognition. However, current theories and implementations suffer from numerous problems and lack of detail. Here we illustrate the problems with two different computational approaches. The Meta-Cognitive Loop and Meta-AQUA both examine the metacognitive reasoning involved in monitoring and reasoning about failures of expectations, and they both learn from such experiences. But neither system presents a full accounting of the variety of known metacognitive phenomena, and, as far as we know, no extant system does. The problem is that no existing cognitive architecture directly addresses metacognition. Instead, current architectures were initially developed to study more narrow cognitive functions and only later were they modified to include higher level attributes. We claim that the solution is to develop a metacognitive architecture outright, and we begin to outline the structure that such a foundation might have.

Earlier Teamwork implies communication with shared references work also evaluates the robustness of ICP against low constrained and symbols. The collaboration between robot and human is environments (Rusinkiewicz and Levoy 2001). This therefore highly dependent on a common representation of was mainly done in simulation so real word datasets targeting the environment. Part of this representation is a map, either this limitations could bring the analysis farther. An other global or local, that can serve both the robot to do its own problem, recently raised in vision registration (Mortensen, task and the human to increase his situation awareness, to Deng, and Shapiro 2005), is the problem of repetitive elements collaboratively plan and observe the evolution of a situation.

In this paper, we consider the issue of guiding ethical behavior in human-robot teams from a systemic viewpoint. Considering a team as a sociotechnical complex, we look at how responsibility for actions can arise through the interaction between the different actors in the team while playing specific roles. We define the notions of role, discuss how they establish a social network, and then use logical notions of multi-agent trust to formalize responsibility as accountability against capabilities that are invoked during collaboration.

Good readers ask questions during reading, and this is presumed to improve their text comprehension. But what about not-so-good readers? Does question asking promote comprehension for struggling readers and, if so, how can we best support these students? This paper examines question generation among low-performing sixth-graders who read moderately-challenging science texts. It characterizes the nature of students’ questions and describes the effects of a video-based peer modeling intervention on their question asking and reading comprehension. In contrast to previous research, this study found that students asked a large number of deep reasoning questions, particularly those related to identifying goals, processes, causes, and consequences. However, such questions were not generally associated with greater understanding. Only two types of deep reasoning questions were related to text comprehension—those that were not answered in the text (directly or indirectly) and those that students labeled as “I’m Confused” questions. The study also found that readers who were exposed to video-based peer modeling of question generation asked more of these types of questions and scored significantly higher on multiple measures of text comprehension. These findings have implications for the design of systems to support struggling readers and for theory-building about question generation.

Social cognition is a key feature of human-level intelligence. However, social reasoning faculties are rarely included in cognitive systems. To encourage research in this direction, we introduce a practical, computational framework that enables socially aware inference. We demonstrate the framework's ability to model a common, complex, and under-investigated aspect of human social behavior: deception. Moreover, we show how a system implementing this framework could dynamically respond once it has detected a lie. We then discuss some of the challenges associated with deception, ending with an outline of future research directions.

As people engage in increasingly complex conversations with computers, the need for generality and flexibility in spoken dialogue systems becomes more apparent. This pa­per describes how three different spoken dialogue systems for the same task reason with knowledge and certainty as they seek to understand what people want. It advocates sys­tems that exploit partial understanding, consider credibility, and are aware both of what they know and of their certainty that it matches their users’ intent.

Beyond belief change and meme adoption, both genetics and infection have been spoken of in terms of information transfer. What we examine here, concentrating on the specific case of transfer between sub-networks, are the differences in network dynamics in these cases: the different network dynamics of germs, genes, and memes. Germs and memes, it turns out, exhibit a very different dynamics across networks. For infection, measured in terms of time to total infection, it is network type rather than degree of linkage between sub-networks that is of primary importance. For belief transfer, measured in terms of time to consensus, it is degree of linkage rather than network type that is crucial. Genes model each of these other dynamics in part, but match neither in full. For genetics, like belief transfer and unlike infection, network type makes little difference. Like infection and unlike belief, on the other hand, the dynamics of genetic information transfer within single and between linked networks are much the same. In ways both surprising and intriguing, transfer of genetic information seems to be robust across network differences crucial for the other two.

Understanding population wide attitude change is an important step to understanding the behavior of societies. In this talk, we will study population wide attitude change through the use of computational models. Using a model based on parallel constraint satisfaction, we will show how varying parameters, such as cognitive effort, and community structure, can impact attitude change in populations.

Ant colony optimization (ACO) algorithms are computational problem-solving methods that are inspired by the complex behaviors of ant colonies; specifically, the ways in which ants interact with each other and their environment to optimize the overall performance of the ant colony. Our eventual goal is to develop and experiment with ACO methods that can more effectively adapt to dynamically changing environments and problems. We describe biological ant systems and the dynamics of their environments and behaviors. We then introduce a family of dynamic ACO algorithms that can handle dynamic modifications of their inputs. We report empirical results, showing that dynamic ACO algorithms can effectively adapt to time-varying environments.

In the context of cognitive architectures, memory is typically considered as a passive storage device with the sole purpose of maintaining and retrieving information relevant to ongoing cognitive processing. If memory is instead considered to be a fundamentally active aspect of cognition, as increasingly suggested by empirically-derived neurophysiological theory, this passive role must be reinterpreted. In this perspective, memory is the distributed substrate of cognition, forming the foundation for cross-modal priming, and hence soft cross-modal coordination. This paper seeks to describe what a cognitive architecture based on this perspective must involve, and initiates an exploration into how human-level cognitive competencies (namely episodic memory, word label conjunction learning, and social behaviour) can be accounted for in such a low-level framework. This proposal of a memory-centred cognitive architecture presents new insights into the nature of cognition, with benefits for computational implementations such as generality and robustness that have only begun to be exploited.