94 research outputs found
A Survey of Brain Inspired Technologies for Engineering
Cognitive engineering is a multi-disciplinary field and hence it is difficult
to find a review article consolidating the leading developments in the field.
The in-credible pace at which technology is advancing pushes the boundaries of
what is achievable in cognitive engineering. There are also differing
approaches to cognitive engineering brought about from the multi-disciplinary
nature of the field and the vastness of possible applications. Thus research
communities require more frequent reviews to keep up to date with the latest
trends. In this paper we shall dis-cuss some of the approaches to cognitive
engineering holistically to clarify the reasoning behind the different
approaches and to highlight their strengths and weaknesses. We shall then show
how developments from seemingly disjointed views could be integrated to achieve
the same goal of creating cognitive machines. By reviewing the major
contributions in the different fields and showing the potential for a combined
approach, this work intends to assist the research community in devising more
unified methods and techniques for developing cognitive machines
Developing the Computational Building Blocks for General Intelligent in SOAR
Cognitive architecture's purpose is to generate artificial agents with capacities similar to the human mind. Soar Cognitive Architecture is to produce the fixed computational building blocks needed for generally intelligent agents— agents that can outright a variety of tasks and encode, use, and learn all types of knowledge to realize the broad cognitive abilities present in humans. This paper introduced an arithmetic agent that does multicolumn, two-digit addition in SOAR. Here, we show the entire calculating procedure, including all of its operators. We are using episodic memory assistance to enhance the set of cognitive abilities that let the agent learn and reason
SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
To realize human-like robot intelligence, a large-scale cognitive
architecture is required for robots to understand the environment through a
variety of sensors with which they are equipped. In this paper, we propose a
novel framework named Serket that enables the construction of a large-scale
generative model and its inference easily by connecting sub-modules to allow
the robots to acquire various capabilities through interaction with their
environments and others. We consider that large-scale cognitive models can be
constructed by connecting smaller fundamental models hierarchically while
maintaining their programmatic independence. Moreover, connected modules are
dependent on each other, and parameters are required to be optimized as a
whole. Conventionally, the equations for parameter estimation have to be
derived and implemented depending on the models. However, it becomes harder to
derive and implement those of a larger scale model. To solve these problems, in
this paper, we propose a method for parameter estimation by communicating the
minimal parameters between various modules while maintaining their programmatic
independence. Therefore, Serket makes it easy to construct large-scale models
and estimate their parameters via the connection of modules. Experimental
results demonstrated that the model can be constructed by connecting modules,
the parameters can be optimized as a whole, and they are comparable with the
original models that we have proposed
- …