Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose

In this paper, a novel architecture for action recognition is developed by applying layers of growing grid neural networks. Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing speed

The problem of finding the similarity between natural language sentences is crucial for many applications in Natural Language Processing (NLP). An accurate calculation of similarity between sentences is highly needed. Many approaches depend on word-to-word similarity to measure sentence similarity. This paper proposes a new approach to improve the accuracy of sentence similarity calculation.The proposed

The field of neuroevolution has achieved much attention in recent years from both academia and industry. Numerous papers have reported its successful applications in different fields ranging from medical domain to autonomous systems. However, it is not clear which evolutionary optimization techniques lead to the best results. In this paper, multilayer perceptron (MLP) neural networks (NNs) are trained

In spite of the recent advancements in the field of deep learning based techniques for facial expression recognition, the efficiency of the state-of-the-art recognition methods in the wild scenarios, remains a challenge. The main reason behind the less efforts made for handling wild scenarios is two-folds: very less and varying levels of cues available to identify the distinguishable patterns of features

To solve low convergence precision and slow convergence speed, a pseudo-dynamic search ant colony optimization algorithm with improved negative feedback mechanism (PACON) is proposed. Firstly, the algorithm introduces an angle in the pheromone transfer rule. Through the rule for calculating the angle, multiple cities with smaller angles are also included in the next candidate city list. It affects

Sparse representation based classification (SRC) has become a popular methodology in face recognition in recent years. One widely used manner is to enforce minimum l1-norm on coding coefficient vector, which is considered as an unsupervised sparsity constraint and usually requires high computational cost. On the other hand, supervised sparsity representation based method (SSR) realizes sparse representation

The actualization of action possibilities (i.e., affordances) can often be accomplished in numerous ways. For instance, an individual could walk over to a rubbish bin to drop an item in or throw the piece of rubbish into the bin from some distance away. The aim of the current study was to investigate the action dynamics that emerge from such under-constrained task or action spaces using an object transportation

Smart environments offer valuable technologies for activity monitoring and health assessment. Here, we describe an integration of robots into smart environments to provide more interactive support of individuals with functional limitations. RAS, our Robot Activity Support system, partners smart environment sensing, object detection and mapping, and robot interaction to detect and assist with activity

This paper presents a geometrical analysis of how local interactions in a large population of categories packed into a feature space create a global structure of feature relevance. The theory is a formal proof that the joint optimization of discrimination and inclusion creates a smooth space of categories such that near categories in the similarity space have similar generalization gradients. Packing

We examine Dubois's (2003) distinction between weak anticipation and strong anticipation. Anticipation is weak if it arises from a model of the system via internal simulations. Anticipation is strong if it arises from the system itself via lawful regularities embedded in the system's ordinary mode of functioning. The assumption of weak anticipation dominates cognitive science and neuroscience and in

A comparative framework of memory processes in males with fragile X syndrome (FXS) and Typically Developing (TYP) mental age-match children is presented. Results indicate a divergence in sequencing skills, such that males with FXS recall sequences similarly to TYP children around five and a half years of age, but eth males with FXS recall significantly worse when compared to TYP children around seven

Emotions and moods color cognition. In this article, we outline how emotions affect judgments and cognitive performance of human agents. We argue that affective influences are due, not to the affective reactions themselves, but to the information they carry about value, a potentially useful finding for creators of artificial agents. The kind of influence that occurs depends on the focus of the agent

BICAs for AI have been happening for decades, realized within multiple cognitive architectures. What is a BICA for AGI? It requires AI to go beyond the limitations of predicative human language, and of predicative logic based on it; also, above human reportable consciousness, to the subconscious/non-conscious level of human (and machine) mind; and then still beyond it. Within machine cognition this

In an attempt to provide a unified account for a vast literature discussing a multiplicity of selves, Gallagher (2013) has proposed a pattern theory of self. Subsequent discussion on this account has led to a concern that the pattern theory, as originally presented, stands as a mere list of aspects that fails to explain how they are related in real-time. We suggest that one way to address these criticisms