In this article we describe a cognitive heuristic known as the unpacking effect by using a mathematical model, based on the quantum formalism, already introduced for the conjunction fallacy. We present the basic postulates of such quantum-like model and we show that the presence of interference terms is responsible of the unpacking effect. In particular, the sign of the interference and its functional form are able to describe the experimental results about subadditivity, superadditivity and additivity. A comparison with previous models is presented, as well as new experimental predictions, allowing to conclude that this new formalism and the basic concepts of quantum information processing provide a new promising way to describe and understand human judgement and categorization.

In order to better understand the mechanisms that lead to resiliency in natural systems, to support decisions that lead to greater resiliency in systems we effect, and to create models that will utilized in highly resilient systems, methods for resiliency analysis will be required. Existing methods and technology for robustness analysis provide a foundation for a rigorous approach to resiliency analysis, but extensions are necessary to address the multiple time scales that must be modeled to understand highly adaptive systems. Further, if resiliency modeling is to be effective, it must be contextualized, requiring that the supporting software will need to mirror the systems being modeling by being pace layered and adaptive.

Narratives structure our understanding of the world and of ourselves. They exploit the shared cognitive structures of human motivations, goals, actions, events, and outcomes. We report on a computational model that is motivated by results in neural computation and captures fine-grained, context sensitive information about human goals, processes, actions, policies, and outcomes. We describe the use of the model in the context of a pilot system that is able to interpret simple stories and narrative fragments in the domain of international politics and economics. We identify problems with the pilot system and outline extensions required to incorporate several crucial dimensions of narrative structure.

Our goal is to develop an interface for older people to effectively communicate with a robotic assistant so that they can safely remain living in their home. We are devising a multimodal interface since people communicate with one another using a variety of verbal and non-verbal signals, including haptics, i.e., physical interactions. We view haptics as an integral component of communication, which in some cases drives the interaction between the user and the robot, and we study its relation to speech and gestures. We illustrate features of interactions between an elderly person and an assistant via excerpts from our ongoing data collection. We also describe the architecture of our interface and ongoing research to bring this interface to fruition.

The AAAI Fall Symposium on Meta-Cognitive Educational - What are the theoretical foundations and how are they articulated Systems: One Step Forward is the second edition of the successful in CBLEs? MCES implemented as CBLEs are designed to interact with - What are the main aspects of metacognition, selfregulation users, and support their learning and decision-making processes. Can MCES actually foster they need to plan their learning activities, to adapt their learners to be self-regulating agents? How can a MCES learning strategies to meet learning goals, become aware of be autonomous and increase its knowledge to match the changing task conditions, and the dynamic aspects of the learners evolving skills and knowledge? MCES may not be embodied, prior to, during, and after they have been involved in but does it help if they act as intentional agents? the learning environment.

In an object manipulation dialogue, a robot may misunderstand an ambiguous command from a user, such as 'Place the cup down (on the table)," potentially resulting in an accident. Although making confirmation questions before all motion execution will decrease the risk of this failure, the user will find it more convenient if confirmation questions are not made under trivial situations. This paper proposes a method for estimating ambiguity in commands by introducing an active learning framework with Bayesian logistic regression to human-robot spoken dialogue. We conducted physical experiments in which a user and a manipulator-based robot communicated using spoken language to manipulate objects.

In the literature on reasoning on the basis of evidence, two traditions exist: one argument-based, and one based on narratives. Recently, we have proposed a hybrid perspective in which argumentation and narratives are combined. This formalized hybrid theory has been tested in a sense-making software prototype for criminal investigators and decision makers. In the present paper, we elaborate on the role of commonsense knowledge. We argue that two kinds of knowledge are essential: argumentation schemes and story schemes. We discuss some of the research issues that need to be addressed.

This paper aims at bringing social robots closer to naive users. A Natural Programming System that allows the end-user to give instructions to a Social Robot has been developed. The instructions derive in a sequence of actions and conditions, that can be executed while the own sequence verbal edition continues. A Dialogue Manager System (DMS) has been developed in a Social Robot. The dialog is described in a voiceXML structure, where a set of information slots is defined. These slots are related to the necessary attributes for the construction of the sequence in execution time. The robot can make specific requests on encountering unfilled slots. Temporal aspects of dialog such as barge-in property, mixed-initiative, or speech intonation control are also considered. Dialog flow is based on Dialog Acts. The dialog specification has also been extended for multimodality management. The presented DMS has been used as a part of a Natural Programming System but can also be used for other multimodal humanrobot interactive skills.

The aim of this paper is to revisit the fundamental requirements for bulding computational models for Interactive Narrative. We express the need for broader computational models of narrative and underline the fundamental difference between models for story generation and models for Interactive Narrative. Research directions are finally sketched to move towards dedicated computational models for Interactive Narrative.

An experiment was conducted to test the efficacy of a new intelligent hypermedia system, MetaTutor, which is intended to prompt and scaffold the use of self-regulated learning (SRL) processes during learning about a human body system. Sixty-eight (N=68) undergraduate students learned about the human circulatory system under one of three conditions: prompt and feedback (PF), prompt-only (PO), and control (C) condition. The PF condition received timely prompts from animated pedagogical agents to engage in planning processes, monitoring processes, and learning strategies and also received immediate directive feedback from the agents concerning the deployment of the processes. The PO condition received the same timely prompts, but did not receive any feedback following the deployment of the processes. Finally, the control condition learned without any assistance from the agents during the learning session. All participants had two hours to learn using a 41-page hypermedia environment which included texts describing and static diagrams depicting various topics concerning the human circulatory system. Results indicate that the PF condition had significantly higher learning efficiency scores, when compared to the control condition. There were no significant differences between the PF and PO conditions. These results are discussed in the context of development of a fully-adaptive hypermedia learning system intended to scaffold self-regulated learning.