Narratives can be defined informally as a “spatio-temporally bounded stream of elementary events”. To make this sort of definition more computationally useful we introduce, firstly, some pragmatic criteria for recognizing highly ambiguous entities like the “elementary events” and for linking these events together into complete narratives. We raise then the problem of how to concretely represent elementary events and narratives in computer-suitable form. We introduce then the main characteristics of a language, NKRL (Narrative Knowledge Representation Language), expressly specified and implemented for dealing with (non-fictional) narratives and temporal information. We conclude by showing briefly how this language can be used for questioning and for particularly complex inference operations.

This paper is part of a larger project to determine how to build agent-based cognitive models capable of initial associative intelligence. Our method here is to take McClelland’s 1981 “Jets and Sharks” dataset and rebuild it using a nonlinear dynamic system with an eye toward determining which parameters are necessary to govern the interactivity of agents in a multi-agent cognitive system. A few number of parameters are suggested concerning diffusion and infusion values, which are basically elementary forms of information entropy, and multi-dimensional overlap from properties to objects and then from objects back to the properties that define them. While no agent-based model is presented, the success of the dynamic systems that are presented here suggest strong starting points for further research in building cognitive complex adaptive systems.

The paper uses the SCOP theory of concepts to model the role of environmental context on three levels of entrepreneurial opportunity: idea generation, idea development, and entrepreneurial decision. The role of contextual-fit in the generation and development of ideas is modeled as the collapse of their superposition state into one of the potential states that composes this superposition. The projection of this collapsed state on the socio-economic basis results in interference between the developed idea and the perceptions of the supporting community, undergoing an eventual collapse for an entrepreneurial decision that reflects the shared vision of its stakeholders. The developed idea may continue to evolve due to continuous or discontinuous changes in the environment. The model offers unique insights into the effects of external influences on entrepreneurial decisions.

When general purpose software agents fail, it's often because they're brittle and need more background commonsense knowledge. In this paper we present relation properties as a valuable type of commonsense knowledge that can be automatically inferred at scale by reading the Web. People base many commonsense inferences on their knowledge of relation properties such as functionality, transitivity, and others. For example, all people know that bornIn(Year) satisfies the functionality property, meaning that each person can be born in exactly one year. Thus inferences like "Obama was born in 1961, so he was not born in 2008", which computers do not know, are obvious even to children. We demonstrate scalable heuristics for learning relation functionality from noisy Web text that outperform existing approaches to detecting functionality. The heuristics we use address Web NLP challenges that are also common to learning other relation properties, and can be easily transferred. Each relation property we learn for a Web-scale set of relations will enable computers to solve real tasks, and the data from learning many such properties will be a useful addition to general commonsense knowledge bases.

Recent work on entailment suggests that natural logics are well-suited to determining whether one sentence lexically entails another. We show how the EPILOG reasoning engine, designed for a natural language-like meaning representation (Episodic Logic, or EL), can be used to emulate natural logic inferences, while also enabling more general inferences such as ones from multiple premises, or ones based on world knowledge. Thus, to exploit the capabilities of EPILOG, we are working to populate its knowledge base with the kinds of lexical knowledge on which natural logics rely.

This position paper provides an overview of work conducted and an outlook of future directions within the field of Information Retrieval (IR) that aims to develop novel models, methods and frameworks inspired by Quantum Theory (QT).

Using computational intelligence, our ultimate goal is to self-regulate systems composed of humans, machines and robots. Self-regulation is important for the control of mixed organizations and systems. An overview of self-regulation for organizations and systems, characterized by our solution of the tradeoffs between Fourier pairs of Gaussian distributions that affect decision-making differently, is provided. A mathematical outline of our solution and a sketch of future plans are provided.

Separability is a concept that is very difficult to define, and yet much of our scientific method is implicitly based upon the assumption that systems can sensibly be reduced to a set of interacting components. This paper examines the notion of separability in the creation of bi-ambiguous compounds that is based upon the CHSH and CH inequalities. It reports results of an experiment showing that violations of the CHSH and CH inequality can occur in human conceptual combination.

This article introduces a "pseudo classical" notion of modelling non-separability. This form of non-separability can be viewed as lying between separability and quantum-like non-separability. Non-separability is formalized in terms of the non-factorizabilty of the underlying joint probability distribution. A decision criterium for determining the non-factorizability of the joint distribution is related to determining the rank of a matrix as well as another approach based on the chi-square-goodness-of-fit test. This pseudo-classical notion of non-separability is discussed in terms of quantum games and concept combinations in human cognition.

Traditional dialogue management systems are tightly coupled with the sensing ability of a single computer. How to organize an interaction in pervasive environments to provide a friendly and integrated interface to users is an important issue. This requires a transition of the human-computer interaction (HCI) from tight coupling to loose coupling. This paper proposes a hierarchical multimodal framework for pervasive interactions. Our system is designed to remind the activities of daily living for individuals with cognitive impairments.The system is composed of Markov decision processes for activity planing, and multimodal partially observable Markov decision processes for action planning and executing. Empirical results demonstrate the hierarchical multimodal framework establishes a flexible mechanism for pervasive interaction systems.