This paper addresses the numerical estimation of the principal curvature of pattern transformation manifolds. When a visual pattern undergoes a geometric transformation, it forms a (sub)manifold in the ambient space, which is usually called the transformation manifold. The manifold curvature is an important property characterizing the manifold geometry, with several applications in manifold learning. We propose an efficient numerical algorithm for estimating the principal curvature at a certain point on the transformation manifold.

This paper proposes a number of social issues that are essential in understanding any given story, and thus, that must be included in a comprehensive approach to computational modeling of narrative. It focuses on oral narratives, and on the social event of the telling of a story. For participants in the telling, the central social issue is the story’s evaluation or meaning: the point or moral of the story. Value or meaning is created relative to social membership, and so, to understand evaluation, it is not sufficient to understand a story solely as a bounded unit. Therefore, this paper examines the ways in which narrative meaning is negotiated between narrator and interlocutors. It demonstrates how a given story can take on different meanings for different audiences. The life course of a story is also proposed as relevant dimension for understanding. Ephemeral stories are distinguished from stories which have multiple tellings, both for the stories of individuals, and for stories which form part of the story stock of institutions. Storytelling rights are also considered: who within a group has the right to tell a particular story on a particular occasion. These issues are proposed as potential meta-data to be used in the analysis of stories. Finally, the paper indicates an area in which computational understanding of narrative, including these social issues, has potential for practical applications: as part of current commercial knowledge capture and archiving activities.

We propose a computational model of joint attention consisting of three parts: responding to joint attention, initiating joint attention, and ensuring joint attention. This model is supported by psychological findings and matches the developmental timeline in humans. We present two experiments that test this model and investigate joint attention in human-robot interaction. The first experiment explored the effects of responding to joint attention on human-robot interaction. We show that robots responding to joint attention are more transparent to humans and are more competent and socially interactive. The second experiment studied the importance of ensuring joint attention in human-robot interaction. Data upheld our hypotheses that a robot's ensuring joint attention behavior yields better performance in human-robot interactive tasks and ensuring joint attention behaviors are perceived as natural behaviors.

Reasoning about Commonsense knowledge poses many problems that traditional logical inference doesn't handle well. Among these is cross-domain inference: how to draw on multiple independently produced knowledge bases. Since knowledge bases may not have the same vocabulary, level of detail, or accuracy, that inference should be "scruffy." The AnalogySpace technique showed that a factored inference approach is useful for approximate reasoning over noisy knowledge bases like ConceptNet. A straightforward extension of factored inference to multiple datasets, called Blending, has seen productive use for commonsense reasoning. We show that Blending is a kind of Collective Matrix Factorization (CMF): the factorization spreads out the prediction loss between each dataset. We then show that blending additional data causes the singular vectors to rotate between the two domains, which enables cross-domain inference. We show, in a simplified example, that the maximum interaction occurs when the magnitudes (as defined by the largest singular values) of the two matrices are equal, confirming previous empirical conclusions. Finally, we describe and mathematically justify Bridge Blending, which facilitates inference between datasets by specifically adding knowledge that "bridges" between the two, in terms of CMF.

MAMmodels are inherent in CAS as a holistic System. Multi-agent modeling is based on "down-up" Many surprising properties of the Economic Systems (such methodology, starting from the interaction of a multitude as sudden crises, jumps of macro-indices, catastrophe-like of "agents" to revealing the emergent properties of the changes of the system) can be understood deeper on the integral system.

We propose a collaborative approach to the issue of resource creation for commonsense computing by developing a collaboratory application aimed at children. Human validation is enabled through a game-with-a-purpose (GWAP) interface, gathering reliability judgements of assertions that can be used to aid the process of resource validation. Our experiments confirm that children aged 10 to 12 can be valuable and reliable partners in building commonsense databases, due to their stage of mental development and their eagerness to play GWAPs. Results show that children adapt their word choice in the assertions they provide to the difficulty level of the stimuli words, and that the judgements gathered through in-game validation can help to validate about 30% of the gathered statements automatically.

Since the early days of quantum theory, the concept of wave function collapse has been looked upon as mathematically unquantifiable, observer-dependent, non-local, or simply inelegant. Consequently, modern interpretations of quantum theory often try to avoid or make irrelevant the need for wave collapse. This is ironic, since experimental quantum physics requires some variant of wave collapse wherever quantum phenomena interact with the classical universe of the observer. This paper proposes a pragmatic view in which wave function collapses are treated as real phenomena that occur in pairs. Paired collapses occur when two wave packets exchange real (vs. virtual) momentum-carrying force particles such as photons. To minimize reversibility, such pairs must be separated by a relativistically time-like interval. The resulting model resembles a network of future-predictive simulations (wave packets) linked together by occasional exchanges of data (force particles). Each data exchange “updates” the wave packets by eliminating the need for them to “consider” some range of possible futures. The rest of the paper explores the information processing implications of this idea of networked wave packets. It is postulated that similar networks of simulations in classical computers could provide faster, more efficient ways to process sensor data.

We propose a simple model of network co-evolution in a game-dynamical system of interacting agents that play repeated games with their neighbors, and adapt their behaviors and network links based on the outcome of those games. The adaptation is achieved through a simple reinforcement learning scheme. We show that the collective evolution of such a system can be described by appropriately defined replicator dynamics equations. In particular, we suggest an appropriate factorization of the agents strategies thats results in a coupled system of equations characterizing the evolution of both strategies and network structure, and illustrate the framework on two simple examples.

This paper presents our work in developing a commonsense knowledge source based on semantic concepts about objects, activities and their relationships in a child’s daily life. This commonsense ontology is then used by our automatic story generator to output children's stories of the fable form from a given input picture. The generated story is a narration of the events of a basic plot that flows from negative to positive (rule violation to value acquisition), using themes that are familiar to children. The paper ends with descriptions of further investigations that are underway to extend the system, including using a formal upper ontology to represent storytelling knowledge, and the generation of stories from a given set of sequential scenes.

In order to understand the role of metacognition and self-regulation in student learning, 35 college students were asked to solve problems in college linear algebra and in remedial math using Cognitive Constructor. Results reveal the predominance of forward chaining in problem solving.