A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI

The article identified 42 cognitive architectures for creating general artificial intelligence (AGI) and proposed a set of interrelated functional blocks that an agent approaching AGI in its capabilities should possess. Since the required set of blocks is not found in any of the existing architectures, the article proposes a new cognitive architecture for intelligent systems approaching AGI in their capabilities. As one of the key solutions within the framework of the architecture, a universal method of knowledge representation is proposed, which allows combining various non-formalized, partially and fully formalized methods of knowledge representation in a single knowledge base, such as texts in natural languages, images, audio and video recordings, graphs, algorithms, databases, neural networks, knowledge graphs, ontologies, frames, essence-property-relation models, production systems, predicate calculus models, conceptual models, and others. To combine and structure various fragments of knowledge, archigraph models are used, constructed as a development of annotated metagraphs. As components, the cognitive architecture being developed includes machine consciousness, machine subconsciousness, blocks of interaction with the external environment, a goal management block, an emotional control system, a block of social interaction, a block of reflection, an ethics block and a worldview block, a learning block, a monitoring block, blocks of statement and solving problems, self-organization and meta learning block

Artwork creation by a cognitive architecture integrating computational creativity and dual process approaches

The paper proposes a novel cognitive architecture (CA) for computational creativity based on the Psi model and on the mechanisms inspired by dual process theories of reasoning and rationality. In recent years, many cognitive models have focused on dual process theories to better describe and implement complex cognitive skills in artificial agents, but creativity has been approached only at a descriptive level. In previous works we have described various modules of the cognitive architecture that allows a robot to execute creative paintings. By means of dual process theories we refine some relevant mechanisms to obtain artworks, and in particular we explain details about resolution level of the CA dealing with different strategies of access to the Long Term Memory (LTM) and managing the interaction between S1 and S2 processes of the dual process theory. The creative process involves both divergent and convergent processes in either implicit or explicit manner. This leads to four activities (exploratory, reflective, tacit, and analytic) that, triggered by urges and motivations, generate creative acts. These creative acts exploit both the LTM and the WM in order to make novel substitutions to a perceived image by properly mixing parts of pictures coming from different domains. The paper highlights the role of the interaction between S1 and S2 processes, modulated by the resolution level which focuses the attention of the creative agent by broadening or narrowing the exploration of novel solutions, or even drawing the solution from a set of already made associations. An example of artificial painter is described in some experimentations by using a robotic platform

Imitating Human Responses via a Dual-Process Model Approach

Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels the intuitive mode as “System 1” and the reflective mode as “System 2”. The current research suggests by leveraging an agent which forms decisions based on a dual-process model, an agent in a human-machine team can maintain a better shared mental model with the user. Evaluation of DPM-MN in a game called Space Navigator shows that DPM-MN presents a successful dual-process theory motivated model

The neural and cognitive mechanisms underlying creative thinking

The ability to generate creative ideas and novel solutions is a defining feature of human cognition. However, the cognitive and neural mechanisms that underlie creative cognition are poorly understood. While recent research has highlighted the roles of distinct associative and controlled processes in creative cognition, supported by the default mode and executive control networks, respectively, it remains unclear how exactly creative ideas are produced by the interactions of these processes and networks, or how creative cognition relates to more fundamental processes like executive functions and working memory (WM). The present thesis aims to examine the neurocognitive basis of creative thinking using a combination of behavioral and fMRI experiments. The need for greater computational modeling in neurocognitive creativity research (NCR) is also discussed. The first study examines how the default mode and executive control networks contribute to creative cognition over time. Results are broadly suggestive of distinct generative and evaluative phases in creative thought. A second study explores relationships between multiple forms of creative thinking and multiple forms of inhibition, finding that divergent thinking is related to cognitive inhibition. In a third study, relationships between creative cognition and control over WM are examined, using measures of executive functions. While no relationships were found between divergent thinking and executive functions, a positive relationship was found between WM updating and convergent thinking and verbal fluency. In a review chapter, the case for greater computational modeling in NCR is made. Previous models of creative cognition, and how these might be improved upon, are discussed, with some examples of the model development process. In a final study, relationships are explored between personality measures and evaluations of the novelty, usefulness, and creativity of ideas. A closing chapter summarizes all findings and discusses avenues for future research