The Mathematical Abstraction Theory, The Fundamentals for Knowledge Representation and Self-Evolving Autonomous Problem Solving Systems
Seppo Ilari Tirri

TL;DR
This paper develops a mathematical framework using generalized free algebra nets for automated problem solving in robotics, enabling self-evolving systems with expanded solving capabilities and formal solution generation methods.
Contribution
It introduces a novel algebraic approach with net rewriting systems, quotient algebras, and homomorphisms for universal abstraction and self-evolution in problem solving systems.
Findings
Construction of solution algorithms parallel to candidate solutions.
Establishment of quotient algebras for class-based problem solving.
Expansion of solving power through new solutions and self-evolution mechanisms.
Abstract
The intention of the present study is to establish the mathematical fundamentals for automated problem solving essentially targeted for robotics by approaching the task universal algebraically introducing knowledge as realizations of generalized free algebra based nets, graphs with gluing forms connecting in- and out-edges to nodes. Nets are caused to undergo transformations in conceptual level by type wise differentiated intervening net rewriting systems dispersing problems to abstract parts, matching being determined by substitution relations. Achieved sets of conceptual nets constitute congruent classes. New results are obtained within construction of problem solving systems where solution algorithms are derived parallel with other candidates applied to the same net classes. By applying parallel transducer paths consisting of net rewriting systems to net classes congruent quotient…
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Evolutionary Algorithms and Applications
