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The evolution and civilization development lead us to the progress next important step - the creation of AGI. More "intelligent" tasks are successfully solved by machine learning methods using neural network algorithms. Not only the solved problems quantitative increase in the number and accuracy are important, but also a qualitative transition to the tasks of obtaining "truly" new knowledge and building "truly" new goals on their basis. This transition can serve as a criterion for determining AGI. The paper shows necessity of using existing knowledge to advance into the field of more and more diverse "truly" new knowledge. Also, the paper shows usefulness of implementing proposed ideas on a scalable hierarchical structure which has two main modes of operation: "intuition" and "thinking", and structure of vector control of these and other operation modes, similar to animal hormonal control. [ABSTRACT FROM AUTHOR]

This paper outlines the experimental framework for an e-governance study by the AGI Laboratory. The goal of this research study is to identify indications of the relative performance of e-governance methodologies and how those methods might be improved by applying advanced agent and collective based AI software. The agent in this study will be based on the Independent Core Observer Model Cognitive Architecture modified with mASI (mediated Artificial Superintelligence) collective system architecture. The study will apply different groups and methods to a static set of questions analyzing the quality of those results. We hope to identify the best application model for e-governance using this kind of technology and help us identify additional paths for research with the mASI research program and systems as applied to e-governance. [ABSTRACT FROM AUTHOR]

This paper shows how the Independent Core Observer Model (ICOM) Cognitive Architecture for Artificial General Intelligence (AGI) can be applied to building a collective intelligence system called a mediated Artificial Superintelligence (mASI). The details include breaking down the ICOM implementation in the form of the mASI system and the general performance of initial studies with the mASI. Details of the primary difference between the Independent Core Observer Model Cognitive Architecture and the mASI architecture variant include inserting humanity in the contextual engine components of ICOM, creating a type of collective intelligence. Humans can 'mediate' new system-generated thinking keeping the thought process accessible and slow enough for humans to oversee and understand. This also allows the modification of emotional valences of the thought process of the mASI system to help the system generate complex contextual models (knowledge graphs) of new ideas and which speeds up the learning process. With the humans acting as control rods in a reactor and emotional drivers, the mASI system maintains safety where the system would cease to function if humans walked away. [ABSTRACT FROM AUTHOR]

This paper articulates the methodology and reasoning for how biasing in the Independent Core Observer Model (ICOM) Cognitive Architecture for Artificial General Intelligence (AGI) is done. This includes the use of a forced western emotional model, the system "needs" hierarchy, fundamental biasing and the application of SSIVA theory at the high level as a basis for emotionally bound ethical and moral experience in ICOM systems and how that is manifested in system behavior and the mathematics that supports that experience or qualia in ICOM based systems. [ABSTRACT FROM AUTHOR]

Fitting a bionically inspired cognitive model to a technical application domain is a challenging task. This work presents the fundamental steps in applying the SiMA-based cognitive model ECABA to the domain of building automation. The focus of the paper is to show the initial steps and challenges for implementing the human inspired mechanisms of drives and social rules when modeling a cognitive control system for a non-human body. We then illustrate the flexibility and extensibility of the intended distributed cognitive approach by presenting the iterative development of a simple reactive system with interacting components. Using a simple use case, we show how the cognitive processes can be implemented and utilized in a multi-agent system to provide a first, reactivity-based control system. [ABSTRACT FROM AUTHOR]