Evgenii E. Vityaev

Cognitive imagination is a type of imagination that plays a key role in human thinking. It is not a ``picture-in-the-head'' imagination. It is a faculty to mentally visualize coherent and holistic systems of concepts and causal links that serve as semantic contexts for reasoning, decision making and prediction. Our position is that the role of cognitive imagination is still greatly underestimated, and this creates numerous problems and diminishes the current capabilities of AI. For instance, when reasoning, humans rely on imaginary contexts to retrieve background info. They also constantly return to the context for semantic verification that their reasoning is still reasonable. Thus, reasoning without imagination is blind. This paper is a call for greater attention to cognitive imagination as the next promising breakthrough in artificial intelligence. As an instrument for simulating cognitive imagination, we propose semantic models -- a new approach to mathematical models that can learn, like neural networks, and are based on probabilistic causal relationships. Semantic models can simulate cognitive imagination because they ensure the consistency of imaginary contexts and implement a glass-box approach that allows the context to be manipulated as a holistic and coherent system of interrelated facts glued together with causal relations.

While AI systems excel at reasoning within formal frameworks, their tendency to hallucinate remains a critical challenge. This paper proposes a task-based approach to enhance reliability. By focusing on the specific task and its resolution criteria, we ensure AI solutions are informed by a deep understanding of the problem’s inherent limitations, including its defining axioms and theorems. This comprehension of the problem’s structure and constraints is key to mitigating hallucination and building trustworthy AI.

In this paper, the problem of digital transformation of the object and the subject of management of the economic actor into their Digital Twins (DTs) is considered. By economic actors we understand digital computer models that provide system historical, operational and forecast data about both the object and the subject of management and the environment. The DTs of economic actors’ subjects of management have been studied very poorly since their construction raises a nontrivial problem of completeness and adequacy of their digital representation. We believe that the main and true content of problem solving is the task solution, and the activity of managing an actor is nothing but tasks! The management of resources, risks, data, changes, etc., has a secondary character and therefore represents a special case of options for solving managerial tasks. The authors propose that the original task approach to artificial intelligence, which takes into account the achievements of the physiological theory of functional systems of purposeful behavior, effectively overcomes the problem of building DTs of economic actors. The concept of a task is carefully analysed: what is its nature; what is the criterion for its solution; how should the task be formulated in terms of executable specifications leading to its solution; how should the task be constructed and solved. The logical and functional model of the subject of management of the economic actor is described in the paper. As a representative example of the subject of management, the urban management system is presented.

The work demonstrates that brain might reflect the external world causal relationships in the form of a logically consistent and prognostic model of reality, which shows up as consciousness. The paper analyses and solves the problem of statistical ambiguity and provides a formal model of causal relationships as probabilistic maximally specific rules. We suppose that brain makes all possible inferences from causal relationships. We prove that the suggested formal model has a property of an unambiguous inference: from consistent premises we infer a consistent conclusion. It enables a set of all inferences to form a consistent model of the perceived world. Causal relationships may create fixed points of cyclic inter-predictable properties. We consider the "natural" classification introduced by John St. Mill and demonstrate that a variety of fixed points of the objects' attributes forms a "natural" classification of the external world. Then we consider notions of "natural" categories and causal models of categories, introduced by Eleanor Rosch and Bob Rehder and demonstrate that fixed points of causal relationships between objects attributes, which we perceive, formalize these notions. If the "natural" classification describes the objects of the external world, and "natural" concepts the perception of these objects, then the theory of integrated information, introduced by G. Tononi, describes the information processes of the brain for "natural" concepts formation that reflects the "natural" classification. We argue that integrated information provides high accuracy of the objects identification. A computer-based experiment is provided that illustrates fixed points formation for coded digits.

The paper considers a non-reductionist theory of consciousness, which is not reducible to theories of reality and to physiological or psychological theories. Following D.I.Dubrovsky's "informational approach" to the "Mind-Brain Problem", we consider the reality through the prism of information about observed phenomena, which, in turn, is perceived by subjective reality through sensations, perceptions, feelings, etc., which, in turn, are information about the corresponding brain processes. Within this framework the following principle of the Information Theory of Consciousness (ITS) development is put forward: the brain discovers all possible causal relations in the external world and makes all possible inferences by them. The paper shows that ITS built on this principle: (1) also base on the information laws of the structure of external world; (2) explains the structure and functioning of the brain functional systems and cellular ensembles; (3) ensures maximum accuracy of predictions and the anticipation of reality; (4) resolves emerging contradictions and (5) is an information theory of the brain's reflection of reality.

The monograph “Strong Artificial Intelligence. On the Approaches to Superintelligence,” referenced by this paper, provides a cross-disciplinary review of Artificial General Intelligence (AGI). As an anthropomorphic direction of research, it considers Brain Principles Programming (BPP)—the formalization of universal mechanisms (principles) of the brain’s work with information, which are implemented at all levels of the organization of nervous tissue. This monograph provides a formalization of these principles in terms of the category theory. However, this formalization is not enough to develop algorithms for working with information. In this paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed by us earlier that modeling cognitive functions, which are based on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K. Anokhin’s Theory of Functional Brain Systems, Eleonor Rosch’s prototypical categorization theory, Bob Rehder’s theory of causal models and “natural” classification. As a result, the formalization of the BPP is obtained and computer examples are given that demonstrate the algorithms operation.