As a cognitive architecture, ICARUS shares many aspects with other systems and the recent proposal for a standard model of human-like minds. But the architecture also commits to a unique combination of additional assumptions that are important. This paper discusses these aspects and proposes them as part of the standard model of human-like minds.
People acquire new knowledge in various ways and this helps them to adapt to changing environment properly. In this paper, we investigatethe interoperation of multiple learning mechanisms within a single system. We extend a cognitive architecture, ICARUS, to have three different modes of learning. Through experiments in a modified Blocks World and a route generation domain, we test and demonstrate the system's ability to get synergistic effects from these learning mechanisms.
A cognitive architecture (Newell, 1990) specifies the infrastructure for an intelligent system that remains constant across different domains and knowledge bases. This infrastructure includes a commitment to formalisms for representing knowledge, memories for storing this domain content, and processes that utilize and acquire the knowledge. Research on cognitive architectures has been closely tied to cognitive modeling, in that they often attempt to explain a wide range of human behavior and, at the very least, desire to support the same broad capabilities as human intelligence.
In this article, I claim that research on cognitive architectures is an important path to the development of general intelligent systems. I contrast this paradigm with other approaches to constructing such systems, and I review the theoretical commitments associated with a cognitive architecture. These entities were intended to have the same intellectual capacity as humans and they were supposed to exhibit their intelligence in a general way across many different domains. I will refer to this research agenda as aimed at the creation of general intelligent systems. Unfortunately, modern artificial intelligence has largely abandoned this objective, having instead divided into many distinct subfields that care little about generality, intelligence, or even systems.