New technologies that take advantage of the emergence massive Internet Things (IoT) and a hyper-connected network environment have rapidly increased in recent years. These are used diverse environments, such as smart factories, digital healthcare, grids, with security concerns. We intend to operate Security Orchestration, Automation Response (SOAR) various environments through new concept defin...

Three key components of an autonomous intelligent system are planning, execution, and learning. This paper describes how the Soar architecture supports planning, execution, and learning in unpredictable and dynamic environments. The tight integration of these components provides reactive execution, hierarchical execution, interruption, on demand planning, and the conversion of deliberate planni...

In this article we demonstrate how knowledge level learning can be performed within the Soar architecture. That is, we demonstrate how Soar can acquire new knowledge that is not deductively implied by its existing knowledge. This demonstration employs Soar’s chunking mechanism a mechanism which acquires new productions from goal-baaed experience as its only learning mechanism. Chunking has prev...

Much of the work in machine learning has focused on demonstrating the efficacy of learning techniques using training and testing phases. On-line learning over the long term places different demands on symbolic machine learning techniques and raises a different set of questions for symbolic learning than for empirical learning. We have instrumented Soar to collect data and characterize the long-...

Cognitive architecture's purpose is to generate artificial agents with capacities similar the human mind. Soar Architecture produce fixed computational building blocks needed for generally intelligent agents— that can outright a variety of tasks and encode, use, learn all types knowledge realize broad cognitive abilities present in humans. This paper introduced an arithmetic agent does multicol...

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Much of the work in machine learning has focused on situations in which there are distinct training and testing phases. However, the recent increase in interest in real-world systems involving scaling up has led to an increased attention to “anytime learning”, i.e., systems in which learning mechanisms are always active. This places different demands on machine learning techniques and raises a ...