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Cognitive Computation

Nazmul Siddique, Hojjat Adeli
Nature-inspired meta-heuristic algorithms have dominated the scientific literature in the areas of machine learning and cognitive computing paradigm in the last three decades. Chemical reaction optimisation (CRO) is a population-based meta-heuristic algorithm based on the principles of chemical reaction. A chemical reaction is seen as a process of transforming the reactants (or molecules) through a sequence of reactions into products. This process of transformation is implemented in the CRO algorithm to solve optimisation problems...
2017: Cognitive Computation
Kyriacos Nikiforou, Pedro A M Mediano, Murray Shanahan
Continuous-time recurrent neural networks are widely used as models of neural dynamics and also have applications in machine learning. But their dynamics are not yet well understood, especially when they are driven by external stimuli. In this article, we study the response of stable and unstable networks to different harmonically oscillating stimuli by varying a parameter ρ, the ratio between the timescale of the network and the stimulus, and use the dimensionality of the network's attractor as an estimate of the complexity of this response...
2017: Cognitive Computation
M W Spratling
Predictive coding has been proposed as a model of the hierarchical perceptual inference process performed in the cortex. However, results demonstrating that predictive coding is capable of performing the complex inference required to recognise objects in natural images have not previously been presented. This article proposes a hierarchical neural network based on predictive coding for performing visual object recognition. This network is applied to the tasks of categorising hand-written digits, identifying faces, and locating cars in images of street scenes...
2017: Cognitive Computation
Pablo Gervás
The plots of stories are known to follow general patterns in terms of their overall structure. This was the basic tenet of structuralist approaches to narratology. Vladimir Propp proposed a procedure for the generation of new tales based on his semi-formal description of the structure of Russian folk tales. This is one of the first existing instances of a creative process described procedurally. The present paper revisits Propp's morphology to build a system that generates instances of Russian folk tales. Propp's view of the folk tale as a rigid sequence of character functions is employed as a plot driver, and some issues that Propp declared relevant but did not explore in detail-such as long-range dependencies between functions or the importance of endings-are given computational shape in the context of a broader architecture that captures all the aspects discussed by Propp...
2016: Cognitive Computation
Kia Dashtipour, Soujanya Poria, Amir Hussain, Erik Cambria, Ahmad Y A Hawalah, Alexander Gelbukh, Qiang Zhou
With the advent of Internet, people actively express their opinions about products, services, events, political parties, etc., in social media, blogs, and website comments. The amount of research work on sentiment analysis is growing explosively. However, the majority of research efforts are devoted to English-language data, while a great share of information is available in other languages. We present a state-of-the-art review on multilingual sentiment analysis. More importantly, we compare our own implementation of existing approaches on common data...
2016: Cognitive Computation
Saliha Minhas, Amir Hussain
Despite legislative attempts to curtail financial statement fraud, it continues unabated. This study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis with data mining on narrative sections of annual reports/10-K form. Different from the features used in similar research, this paper extracts three distinct sets of features from a newly constructed corpus of narratives (408 annual reports/10-K, 6.5 million words) from fraud and non-fraud firms. Separately each of these three sets of features is put through a suite of classification algorithms, to determine classifier performance in this binary fraud/non-fraud discrimination task...
2016: Cognitive Computation
Florian Damerow, Andreas Knoblauch, Ursula Körner, Julian Eggert, Edgar Körner
Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome...
2016: Cognitive Computation
Kevin Warwick, Huma Shah
In this paper we look at the phenomenon that is the Turing test. We consider how Turing originally introduced his imitation game and discuss what this means in a practical scenario. Due to its popular appeal we also look into different representations of the test as indicated by numerous reviewers. The main emphasis here, however, is to consider what it actually means for a machine to pass the Turing test and what importance this has, if any. In particular does it mean that, as Turing put it, a machine can "think"...
2016: Cognitive Computation
Hussein A Abbass, Eleni Petraki, Kathryn Merrick, John Harvey, Michael Barlow
This paper considers two emerging interdisciplinary, but related topics that are likely to create tipping points in advancing the engineering and science areas. Trusted Autonomy (TA) is a field of research that focuses on understanding and designing the interaction space between two entities each of which exhibits a level of autonomy. These entities can be humans, machines, or a mix of the two. Cognitive Cyber Symbiosis (CoCyS) is a cloud that uses humans and machines for decision-making. In CoCyS, human-machine teams are viewed as a network with each node comprising humans (as computational machines) or computers...
2016: Cognitive Computation
Maria Teresa Llano, Simon Colton, Rose Hepworth, Jeremy Gow
The invention of fictional ideas (ideation) is often a central process in the creative production of artefacts such as poems, music and paintings, but has barely been studied in the computational creativity community. We present here a general approach to automated fictional ideation that works by manipulating facts specified in knowledge bases. More specifically, we specify a number of constructions which, by altering and combining facts from a knowledge base, result in the generation of fictions. Moreover, we present an instantiation of these constructions through the use of ConceptNet, a database of common sense knowledge...
2016: Cognitive Computation
Nazmul Siddique, Hojjat Adeli
This paper presents an overview of significant advances made in the emerging field of nature-inspired computing (NIC) with a focus on the physics- and biology-based approaches and algorithms. A parallel development in the past two decades has been the emergence of the field of computational intelligence (CI) consisting primarily of the three fields of neural networks, evolutionary computing and fuzzy logic. It is observed that NIC and CI intersect. The authors advocate and foresee more cross-fertilisation of the two emerging fields...
2015: Cognitive Computation
Chenghua Lin, Dong Liu, Wei Pang, Zhe Wang
In this paper, we present a semi-automatic system (Sherlock) for quiz generation using linked data and textual descriptions of RDF resources. Sherlock is distinguished from existing quiz generation systems in its generic framework for domain-independent quiz generation as well as in the ability of controlling the difficulty level of the generated quizzes. Difficulty scaling is non-trivial, and it is fundamentally related to cognitive science. We approach the problem with a new angle by perceiving the level of knowledge difficulty as a similarity measure problem and propose a novel hybrid semantic similarity measure using linked data...
2015: Cognitive Computation
Zujian Wu, Wei Pang, George M Coghill
Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems. In the proposed framework, interactions between reactants in the candidate models for a target biochemical system are evolved and eventually identified by the application of a qualitative model learning approach with an evolution strategy...
2015: Cognitive Computation
Michał Czubenko, Zdzisław Kowalczuk, Andrew Ordys
The paper presents and discusses a system (xDriver) which uses an Intelligent System of Decision-making (ISD) for the task of car driving. The principal subject is the implementation, simulation and testing of the ISD system described earlier in our publications (Kowalczuk and Czubenko in artificial intelligence and soft computing lecture notes in computer science, lecture notes in artificial intelligence, Springer, Berlin, 2010, 2010, In Int J Appl Math Comput Sci 21(4):621-635, 2011, In Pomiary Autom Robot 2(17):60-5, 2013) for the task of autonomous driving...
2015: Cognitive Computation
Bernhard J Mitterauer
A new model of the reticular formation of the brainstem is proposed. It refers to the neuronal and glial cell systems. Thus, it is biomimetically founded. The reticular formation generates modes of behavior (sleeping, eating, etc.) and commands all behavior according to the most appropriate environmental information. The reticular formation works on an abductive logic and is dominated by a redundancy of potential command. Formally, a special mode of behavior is represented by a comprehensive cycle (Hamilton loop) located in the glial network (syncytium) and embodied in gap junctional plaques...
2015: Cognitive Computation
Paul A Wilson, Barbara Lewandowska-Tomaszczyk
If robots are to successfully interact with humans, they need to measure, quantify and respond to the emotions we produce. Similar to humans, the perceptual cue inputs to any modelling that allows this will be based on behavioural expression and body activity features that are prototypical of each emotion. However, the likely employment of such robots in different cultures necessitates the tuning of the emotion feature recognition system to the specific feature profiles present in these cultures. The amount of tuning depends on the relative convergence of the cross-cultural mappings between the emotion feature profiles of the cultures where the robots will be used...
2014: Cognitive Computation
Dag Sverre Syrdal, Kerstin Dautenhahn, Kheng Lee Koay, Wan Ching Ho
This article describes the prototyping of human-robot interactions in the University of Hertfordshire (UH) Robot House. Twelve participants took part in a long-term study in which they interacted with robots in the UH Robot House once a week for a period of 10 weeks. A prototyping method using the narrative framing technique allowed participants to engage with the robots in episodic interactions that were framed using narrative to convey the impression of a continuous long-term interaction. The goal was to examine how participants responded to the scenarios and the robots as well as specific robot behaviours, such as agent migration and expressive behaviours...
2014: Cognitive Computation
Simon Hunt, Qinggang Meng, Chris Hinde, Tingwen Huang
This paper looks at consensus algorithms for agent cooperation with unmanned aerial vehicles. The foundation is the consensus-based bundle algorithm, which is extended to allow multi-agent tasks requiring agents to cooperate in completing individual tasks. Inspiration is taken from the cognitive behaviours of eusocial animals for cooperation and improved assignments. Using the behaviours observed in bees and ants inspires decentralised algorithms for groups of agents to adapt to changing task demand. Further extensions are provided to improve task complexity handling by the agents with added equipment requirements and task dependencies...
2014: Cognitive Computation
Patryk A Laurent
Decision-making often requires taking into consideration immediate gains as well as delayed rewards. Studies of behavior have established that anticipated rewards are discounted according to a decreasing hyperbolic function. Although mathematical explanations for reward delay discounting have been offered, little has been proposed in terms of neural network mechanisms underlying discounting. There has been much recent interest in the potential role of the hippocampus. Here we demonstrate that a previously-established neural network model of hippocampal region CA3 contains a mechanism that could explain discounting in downstream reward-prediction systems (e...
March 1, 2013: Cognitive Computation
Paul C Bressloff, Stephen Coombes
In this paper, we revisit the work of John G Taylor on neural 'bubble' dynamics in two-dimensional neural field models. This builds on original work of Amari in a one-dimensional setting and makes use of the fact that mathematical treatments are much simpler when the firing rate function is chosen to be a Heaviside. In this case, the dynamics of an excited or active region, defining a 'bubble', reduce to the dynamics of the boundary. The focus of John's work was on the properties of radially symmetric 'bubbles', including existence and radial stability, with applications to the theory of topographic map formation in self-organising neural networks...
2013: Cognitive Computation
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