Conceptual Blending through the process of Double Scope Blending provides an account for human creativity. We show how computational creativity can be modeled after Double Scope Blending for machine generation of scenarios, stories, hypotheses, etc. This paper describes an application of this process to the generation of novel and creative scenarios in the maritime security domain.

This paper claims for a shift towards "the formal sciences" in the cognitive sciences. In order to explain the phenomenon of cognition, including aspects such as learning and intelligence, it is necessary to explore the concepts and methodologies offered by the formal sciences. In particular, category theory is proposed as the most fitting tool for the building of an unified theory of cognition. This paper proposes a radically new view based in category theory is provided. A cognitive model is informally defined as a mapping between two different structures, while a structure is the set of components of a system and their relationships. Put formally in categorical terms, a model is a functor between categories that reflects the structural invariance between them. In the paper, the theory of categories is presented as the best possible framework to deal with complex system modeling -ie: biologically inspired systems that transcend and offer a much more powerful tool kit to deal with the phenomenon of cognition that other purely verbal tools like the psychological categories that Rosch or Harnad refer.

Early studies of public budgeting emphasized uncertainty Two of us (BJ and FB) have published a set of papers, in the decision-making environment. Budgeting in the books focusing on annual budget changes (Jones and absence of information about the impacts of decisions led Baumgartner 2005b). Leptokurtic distribution of percentual to an adjustment process rooted in simple decision rules budget changes were observed in a broad range of settings: and bargaining among interests. This led to marginal or small increases and small decreases of budgets and budget incremental adjustments from the budgetary status quo, components are the most frequent, but time to time large with all major actors wary of big changes to the budgetary increases and cutoffs are observed as well.

This paper advocates the use of a portfolio of representations for problem solving in complex domains. It describes an approach that decouples efficient storage mechanisms called descriptives from the decision-making procedures that employ them. An architecture that takes this approach can learn which representations are appropriate for a given problem class. Examples of search with a portfolio of representations are drawn from a broad set of domains.

The purpose of this study was to develop and test a platform that would assist the Environmental Protection Agency (EPA), and the scientific community at large, in the generation of a human activity and energy expenditure database of sufficient detail to accurately predict human exposures and dose to various pollutants. The monitoring system developed is easily extendable to the collection of other health-related data. Our protocol tested the use of a digital voice recorder to collect activity/location diary data assuming it to be a less burdensome and a more reliable method than using paper and pencil diaries or hand-held computers. We expected the data to be more complete and reliable than retrospective reports (diaries filled out at the end of day) because the recorders are easy to use, the diary entries are made as the events occur, and we expected that participants would be more likely to complete the study because of the reduced burden. The data collection plan was also expected to show that the cost of the transcription of the diary can be reduced substantially by using speech and language processing to translate the digital diaries into the EPA’s Comprehensive Human Activity Database (CHAD).

Simulation techniques used to generate complex biological models are becoming promising research tools in oncology. Using a general Complex Adaptive Systems model that can be tailored to map various phenomena, here, we describe how this model applies to tumor growth. The multi-agent modeling environment is generated using Netlogo. The stochastic model consists of active objects including normal immune and cancer cells. The simulations conducted mimicked the tumor progression success and failure and the status of the tumor mass despite constant variations remained stable for an extended time. Furthermore, increasing the efficiency of the immune cells led to decreases in tumor cell numbers variable in both occurrence time and duration.

A novel analytical methodology has proven fruitful in developing a functional identification of consciousness with operable mental control structure in human higher brain function. Two operational homologies (one associated with language, the other tool use) derived from mammalian instrumental behavioral competence are identified, each exadaptively accessible: one a specialization of attentive search to (conventional, linguistic) internalized symbolic lexicon; the second being a combination – a co-parallel activation – of symbolically specialized attention with the original external ‘spotlight’ in order to support (deliberative, choice-making) navigational tasking. The mechanism by which consciousness becomes articulated to support the specialized control requirements of three cognitive performance levels is described, in particular for the case of the social bipedal hominid. A single articulated template model is posed to intervene between the incoherent neuronal and the coherently conscious mental level of higher brain operation. This cognitive system theory logic lends itself to an explanation of the exadaptive acquisition of a cognitively objectifiable self-model from within subjective experience, and a plausible heuristic for the systematic building of self-aware mental repertoire is discovered.

There are two aspects of practical reasoning which present particular difficulties for current approaches to modelling practical reasoning through argumentation: temporal aspects, and the intrinsic worth of actions. Time is important because actions change the state of the world, we need to consider future states as well as past and present ones. Equally, it is often not what we do but the way that we do it that matters: the same future state may be reachable either through desirable or undesirable actions, and often also actions are done for their own sake rather than for the sake of their consequences. In this paper we will present a semantics for practical reasoning, based on a formalisation developed originally for reasoning about commands, in which actions and states are treated as of equal status. We will show how using these semantics facilitates the handling of the temporal aspects of practical reasoning, and enables, where appropriate, justification of actions without reference to their consequences.

In earlier work, we have shown how a cognitive architecture can be augmented with a diagrammatic reasoning system to produce a bimodal cognitive architecture. In this paper, we show how this bimodal architecture is also bi-representational (multi-representational in the general case) by describing a desiderata for representational formalisms and showing how the diagrammatic representation in biSoar satisfies these requirements.

The goal of this paper is to present four key assumptions regarding the measurement of cognitive and metacognitive regulatory processes used during learning with hypermedia. First, we assume it is possible to detect, trace, model, and foster SRL processes during learning with hypermedia. Second, understanding the complex nature of the regulatory processes during learning with hypermedia is critical in determining why certain processes are used throughout a learning task. Third, it is assumed that the use of SRL processes can dynamically change over time and that they are cyclical in nature (influenced by internal and external conditions and feedback mechanisms). Fourth, capturing, identifying, and classifying SRL processes used during learning with hypermedia is a rather challenging task.