In a dynamic social or biological environment, the interactions between the actors can undergo large and systematic changes. In this paper we propose a model-based approach to analyze what we will refer to as the dynamic tomography of such time-evolving networks. Our approach offers an intuitive but powerful tool to infer the semantic underpinnings of each actor, such as its social roles or biological functions, underlying the observed network topologies. Our model builds on earlier work on a mixed membership stochastic blockmodel for static networks, and the state-space model for tracking object trajectory. It overcomes a major limitation of many current network inference techniques, which assume that each actor plays a unique and invariant role that accounts for all its interactions with other actors; instead, our method models the role of each actor as a time-evolving mixed membership vector that allows actors to behave differently over time and carry out different roles/functions when interacting with different peers, which is closer to reality. We present an efficient algorithm for approximate inference and learning using our model; and we applied our model to analyze a social network between monks (i.e., the Sampson's network), a dynamic email communication network between the Enron employees, and a rewiring gene interaction network of fruit fly collected during its full life cycle. In all cases, our model reveals interesting patterns of the dynamic roles of the actors.

In this paper, a new reinforcement learning approach is proposed which is based on a powerful concept named Active Learning Method (ALM) in modeling. ALM expresses any multi-input-single-output system as a fuzzy combination of some single-input-singleoutput systems. The proposed method is an actor-critic system similar to Generalized Approximate Reasoning based Intelligent Control (GARIC) structure to adapt the ALM by delayed reinforcement signals. Our system uses Temporal Difference (TD) learning to model the behavior of useful actions of a control system. The goodness of an action is modeled on Reward- Penalty-Plane. IDS planes will be updated according to this plane. It is shown that the system can learn with a predefined fuzzy system or without it (through random actions).

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.