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Ang J.H.B.,Cognition and Fusion Laboratory | Teow L.N.,Cognition and Fusion Laboratory | Ng G.W.,Cognition and Fusion Laboratory
15th International Conference on Information Fusion, FUSION 2012 | Year: 2012

This paper proposes a Computational Cognitive Model (CCM) inspired by the biological mirror neurons and the theory of mind reading for high level information fusion, in particular, intent inference and action prediction. Existing computational prediction models in the literature would usually build the other person's mental model from scratch; however, this would not work if there is no knowledge about the other person initially. Instead, this paper uses one's own mental model at the start for prediction of the other person and does a perspective changing (or mirroring), i.e., putting oneself in the other person's shoes, to infer the other's thoughts and actions. A model analysis using simulated data is carried out and results show that by using mirroring principles, convergence to the other person's mental model is faster than using baseline method (prediction with an equal probability distribution), and it is also able to give higher prediction accuracy. In addition, feedback and updating mechanisms in the proposed model help to ensure convergence towards the other person's mental model. © 2012 ISIF (Intl Society of Information Fusi). Source


Ng G.W.,Cognition and Fusion Laboratory | Tan Y.S.,Cognition and Fusion Laboratory | Xiao X.X.,Cognition and Fusion Laboratory | Chan R.Z.,Cognition and Fusion Laboratory
Communications in Computer and Information Science | Year: 2012

The paper presents the usage of a prototype DSO cognitive architecture (DSO-CA) for mobile surveillance. DSO-CA is able to bring to bear different types of knowledge to solve problems. It imbues mobile robots with intelligent capabilities like reasoning and adaptive path planning in dynamic environments, and achieves human-inspired object recognition. These intelligent robotic movements and object recognition capabilities can potentially be applied to mobile surveillance to enhance security situational awareness. © 2012 Springer-Verlag. Source


Pan X.,Cognition and Fusion Laboratory | Teow L.N.,Cognition and Fusion Laboratory | Tan K.H.,Cognition and Fusion Laboratory | Ang J.H.B.,Cognition and Fusion Laboratory | Ng G.W.,Cognition and Fusion Laboratory
15th International Conference on Information Fusion, FUSION 2012 | Year: 2012

This paper seeks to investigate how the existing knowledge in a cognitive system can be used to help fill-in an incomplete situation picture. This is motivated by the human brain's innate ability to use new incoming information together with stored knowledge to fill in gaps of the whole picture in the mind. The research focus of this paper is on the inference or retrieval of information that is relevant to the current circumstances, but not directly obtainable from actual observations. The objective is to derive methods to help uncover as complete a situation picture as possible with high degree of confidence. Two computational approaches to enhance situation awareness for adaptive decision making given partial information are proposed. The proposed approaches enhanced an existing system Dynamic Bayesian Reasoning and Advanced Intelligent Network (D'Brain) which is a cognitive based dynamic reasoning system that uses Bayesian network as the underlying knowledge representation. Simulations are carried out to test the feasibility of the proposed approaches. The results show that they are promising approaches to fulfil the objectives in enhancing situation awareness for adaptive decision making. © 2012 ISIF (Intl Society of Information Fusi). Source


Ng G.W.,Cognition and Fusion Laboratory | Tan Y.S.,Cognition and Fusion Laboratory | Teow L.N.,Cognition and Fusion Laboratory | Ng L.H.,Cognition and Fusion Laboratory | And 2 more authors.
Artificial General Intelligence - Proceedings of the Third Conference on Artificial General Intelligence, AGI 2010 | Year: 2010

A cognitive architecture specifies a computational infrastructure that defines the various regions/functions working as a whole to produce human-like intelligence [1]. It also defines the main connectivity and information flow between various regions/functions. These functions and the connectivity between them in turn facilitate and provide implementation specifications for a variety of algorithms. Drawing inspirations from Computational Science, Neuroscience and Psychology, a top-level cognitive architecture which models the information processing in human brain is developed. Three key design principles [2] inspired by the brain - Hierarchical Structure, Distributed Memory and Parallelism - are incorporated into the architecture. A prototype cognitive system is developed and it is able to bring to bear different types of knowledge to solve a problem. It has been applied to object recognition in images. The cognitive system is able to exploit bottom up perceptual information, top down contextual knowledge and visual feedback in a way similar to how human utilizes different knowledge to recognize objects in images. Source

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