Warisa Sritriratanarak

Sushish Baral, Paulo Garcia, Warisa Sritriratanarak

The identification of repeating patterns in discrete grids is rudimentary within symbolic reasoning, algorithm synthesis and structural optimization across diverse computational domains. Although statistical approaches targeting noisy data can approximately recognize patterns, symbolic analysis utilizing deterministic extraction of periodic structures is underdeveloped. This paper aims to fill this gap by employing a hierarchical algorithm that discovers exact tessellations in finite planar grids, addressing the problem where multiple independent patterns may coexist within a hierarchical structure. The proposed method utilizes composite discovery (dual inspection and breadth-first pruning) for identifying rectangular regions with internal repetition, normalization to a minimal representative form, and prime extraction (selective duplication and hierarchical memoization) to account for irregular dimensions and to achieve efficient computation time. We evaluate scalability on grid sizes from 2x2 to 32x32, showing overlap detection on simple repeating tiles exhibits processing time under 1ms, while complex patterns which require exhaustive search and systematic exploration shows exponential growth. This algorithm provides deterministic behavior for exact, axis-aligned, rectangular tessellations, addressing a critical gap in symbolic grid analysis techniques, applicable to puzzle solving reasoning tasks and identification of exact repeating structures in discrete symbolic domains.

Warisa Sritriratanarak, Paulo Garcia

We describe a formulation of multi-agents operating within a Cyber-Physical System, resulting in collaborative or adversarial games. We show that the non-determinism inherent in the communication medium between agents and the underlying physical environment gives rise to environment evolution that is a probabilistic function of agents' strategies. We name these emergent properties Cyber Physical Games and study its properties. We present an algorithmic model that determines the most likely system evolution, approximating Cyber Physical Games through Probabilistic Finite State Automata, and evaluate it on collaborative and adversarial versions of the Iterated Boolean Game, comparing theoretical results with simulated ones. Results support the validity of the proposed model, and suggest several required research directions to continue evolving our understanding of Cyber Physical System, as well as how to best design agents that must operate within such environments.

Warisa Sritriratanarak, Paulo Garcia

Precisely defining consciousness and identifying the mechanisms that effect it is a long-standing question, particularly relevant with advances in artificial intelligence. The scientific community is divided between physicalism and natural dualism. Physicalism posits consciousness is a physical process that can be modeled computationally; natural dualism rejects this hypothesis. Finding a computational model has proven elusive, particularly because of conflation of consciousness with other cognitive capabilities exhibited by humans, such as intelligence and physiological sensations. Here we show such a computational model that precisely models consciousness, natural or artificial, identifying the structural and functional mechanisms that effect it, confirming the physicalism hypothesis. We found such a model is obtainable when including the underlying (biological or digital) substrate and accounting for reactive behavior in substrate sub-systems (e.g., autonomous physiological responses). Results show that, unlike all other computational processes, consciousness is not independent of its substrate and possessing it is an evolutionary advantage for intelligent entities. Our result shows there is no impediment to the realization of fully artificial consciousness but, surprisingly, that it is also possible to realize artificial intelligence of arbitrary level without consciousness whatsoever, and that there is no advantage in imbuing artificial systems with consciousness.

Warisa Sritriratanarak, Paulo Garcia

Without an agreed-upon definition of intelligence, asking "is this system intelligent?"" is an untestable question. This lack of consensus hinders research, and public perception, on Artificial Intelligence (AI), particularly since the rise of generative- and large-language models. Most work on precisely capturing what we mean by "intelligence" has come from the fields of philosophy, psychology, and cognitive science. Because these perspectives are intrinsically linked to intelligence as it is demonstrated by natural creatures, we argue such fields cannot, and will not, provide a sufficiently rigorous definition that can be applied to artificial means. Thus, we present an argument for a purely functional, black-box definition of intelligence, distinct from how that intelligence is actually achieved; focusing on the "what", rather than the "how". To achieve this, we first distinguish other related concepts (sentience, sensation, agency, etc.) from the notion of intelligence, particularly identifying how these concepts pertain to artificial intelligent systems. As a result, we achieve a formal definition of intelligence that is conceptually testable from only external observation, that suggests intelligence is a continuous variable. We conclude by identifying challenges that still remain towards quantifiable measurement. This work provides a useful perspective for both the development of AI, and for public perception of the capabilities and risks of AI.