WLIMES, the Wandering LIMES: Towards a Theoretical Framework for Wandering Logic Intelligence Memory Evolutive Systems

Abstract

This paper compares two complementary theories, Simeonov's Wandering Logic Intelligence and Ehresmann's & Vanbremeersch's Memory Evolutive Systems, in view of developing a common framework for the study of multiscale complex systems such as living systems. It begins by a brief summary of WLI and MES, then analyzes their resemblances and differences. Finally, the article provides an outlook for a future research.Keywords: Wandering logic intelligence; Memory evolutive systems; Hierarchy; Emergence; Self-organization; Autopoiesis; Genetic transcoding; Virtualization; Distribution; Non-locality; Category theory. IntroductionThis paper is divided in 3 parts:(i) The first one gives a brief summary on the Wandering Logic Intelligence (WLI); a biology-inspired theoretical and practical framework for designing evolutionary communication architectures and their services and applications in terms of an always growing model of ever changing software and hardware. The WLI approach represents a next step of network virtualization and evolution of application-and user-aware networks as adaptive systems consolidating both network element and infrastructure flexibility. Now this approach is taken back to biology to model the operational semantics of complex emergent formations and processes.(ii) The second part presents the main ideas at the basis of the Memory Evolutive Systems; a dynamic model for self-organized multi-scale complex systems such as living, cognitive or social systems; these systems have a hierarchy of components changing over time, and their dynamic is modulated by the cooperation/competition between a net of agents, the coregulators, each operating locally with its own rhythm, function and logic. The model is based on a dynamic category theory which gives tools for representing the notion of hierarchy. It emphasizes 2 main properties of such systems: the Multiplicity Principle, a kind of 'flexible redundancy' which is shown to be at the root of the emergence of higher complexity, robustness and flexibility; and the synchronicity laws that the co-regulators must respect and which generate cascades of failures/repair at different levels.

On some recent insights in Integral Biomathics

“…The synergies between MES and WLI were already studied in (Ehresmann & Simeonov, 2012). Both theories taken together appear to provide a sound base for the modeling of biological systems.…”

“…The above enumeration of areas of application addressed by Integral Biomathics techniques is incomplete. For example, the WLIMES approach (Ehresmann & Simeonov, 2012) is able to develop a robust computational framework on top of a qualitative formal model of living systems based on an evolutionary category theory in the domain of personalized medicine addressing such fields as:…”

“…21 incl. evolution, ecology, society and the researcher engaging in scientific enquiry, theorizing, measuring and communicating results as part of the context being investigated A first effort to estimate the match and the benefits of unifying these two approaches was made in the WLIMES concept (Ehresmann and Simeonov, 2012). Later, in discussions with other colleagues, we realized that an appropriate integration of structural visualization techniques for representation, discovery and validation/verification, as well as the implementation of modern programming paradigms, into our tailored qualitative approach will be crucial for its success.…”

Stepping beyond the Newtonian Paradigm in Biology

“…In particular the temporal constrains of each CR must be respected, and hence the synchronicity laws relating the period of a CR to the stability span of the intervening components and the transmission delays between them; these laws are indicated by (Ehresmann & Simeonov, 2012) in this volume. In MES the agents are the coregulators (CRs).…”

Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning

“…Finally, the later version of the theorem has been further generalized to allow recombination over arbitrary set covers, rather than being limited to equivalence relations using the particular case in [7] in conjunction with the tools mentioned above in [14]. While the original purpose of the last two finite-population Geiriger-like theorems is taking advantage of the intrinsic similarities within the state-action set encountered by a learning agent to evaluate actions with the aim of selecting an optimal one, the parallel algorithms on the evolving digraph where the nodes are states and actions are edges from a current state to the one obtained upon executing an action, that are motivated by the theorems exhibit similarities to the way Hebbian learning takes place in biological neural networks that is further supported by Andree Ehresmann's category-theoretic model of cognitive processes called a "Memory Evolutive System" (see, for instance, [15], [16], [17], [18] and many more related articles). In the current article we explain the new theorems as well as the algorithms they motivate, how such algorithms may fit the Memory Evolutive Systems model that hopefully provides a deeper understanding of cognitive processes and makes a further step in the development of intelligent computational systems.…”