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 goalbased experience — as its only learning mechanism. Chunking has p...

Cognitive architectures—task-general theories of the structure and function of the complete cognitive system—are sometimes argued to be more akin to frameworks or belief systems than scientific theories. The argument stems from the apparent non-falsifiability of existing cognitive architectures. Newell (1990) was aware of this criticism and argued that architectures should be viewed not as theo...

Water-jug problem is a famous problem in the field of artificial intelligence, computer programming, recreational mathematics and psychology. Classical methods used to solve this problem are Depth first search, Breadth first search, Diophantine approach, etc. These methods are memory and time consuming. This paper implemented a cognitive approach with two new methods to solve water jug problem ...

Cooper and Shallice (1995) raise many issues regarding the unified theories of cognition research program in general, and Soar in particular. In this paper, we examine one specific criticism of Newell’s (1990) treatment of immediate behavior and use it to explain the notion of the modeling idiom within a cognitive architecture. We compare a dual-task model using Newell’s architecture and idiom ...

This paper presents a brief description of Explanation-Based Learning (EBL), and argues that it is an approach to machine learning with signi cant potential for use in discourse processing. More speci cally, EBL can be used by systems that model discourse generation as goaldriven behavior, and that model discourse interpretation as recognizing the speaker's discourse goals. As evidence, we desc...

A structure-preserving dimension reduction algorithm for large-scale second-order dynamical systems is presented. It is a projection method based on a second-order Krylov subspace. A second-order Arnoldi (SOAR) method is used to generate an orthonormal basis of the projection subspace. The reduced system not only preserves the second-order structure but also has the same order of approximation ...

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...