Toward Global Complex Systems Control - The Autonomous Intelligence Challenge

Complex systems are the emerging new scientific frontier with modern technology advance and new parametric domains study in natural systems.An important challenge is,contrary to classical systems studied so far,the great difficulty in predicting their future behaviour from initial time because,by their very structure,interactions strength between system components is shielding completely their specific individual features.Independent of clear existence of strict laws complex systems are obeying like classical systems,it is however possible today to develop methods allowing to handle dynamical properties of such systems and to master their evolution.So the methods should be imperatively adapted to representing system self organization when becoming complex.This rests upon the new paradigm of passing from classical trajectory space to more abstract trajectory manifolds associated to natural system invariants characterizing complex system dynamics.The methods are basically of qualitative nature,independent of system state space dimension and,because of its generic impreciseness,privileging robustness to compensate for not well known system parameters and functional variations.This points toward the importance of control approach for complex system study in adequate function spaces,the more as for industrial applications there is now evidence that transforming a complicated man made system into a complex one is extremely beneficial for overall performance improvement.But this last step requires larger intelligence delegation to the system requiring more autonomy for exploiting its full potential.A well-defined,meaningful and explicit control law should be set by using equivalence classes within which system dynamics are forced to stay,so that a complex system described in very general terms can behave in a prescribed way for fixed system parameters value.Along the line traced by Nature for living creatures,the delegation is expressed at lower level by a change from regular trajectory space control to task space control following system reassessment into its complex stage imposed by the high level of interactions between system constitutive components.Aspects of this situation with coordinated action on both power and information fluxes are handled in a new and explicit control structure derived from application of Fixed Point Theorem which turns out to better perform than (also explicit) extension of Popov criterion to more general nonlinear monotonically upper bounded potentials bounding system dynamics discussed here.An interesting observation is that when correctly amended as proposed here,complex systems are not as commonly believed a counterexample to reductionism so strongly influential in Science with Cartesian method supposedly only valid for complicated systems.

Compositional Grounded Language for Agent Communication in Reinforcement Learning Environment

In a context of constant evolution of technologies for scientific,economic and social purposes,Artificial Intelligence(AI)and Internet of Things(IoT)have seen significant progress over the past few years.As much as Human-Machine interactions are needed and tasks automation is undeniable,it is important that electronic devices(computers,cars,sensors…)could also communicate with humans just as well as they communicate together.The emergence of automated training and neural networks marked the beginning of a new conversational capability for the machines,illustrated with chat-bots.Nonetheless,using this technology is not sufficient,as they often give inappropriate or unrelated answers,usually when the subject changes.To improve this technology,the problem of defining a communication language constructed from scratch is addressed,in the intention to give machines the possibility to create a new and adapted exchange channel between them.Equipping each machine with a sound emitting system which accompany each individual or collective goal accomplishment,the convergence toward a common“language”is analyzed,exactly as it is supposed to have happened for humans in the past.By constraining the language to satisfy the two main human language properties of being ground-based and of compositionality,rapidly converging evolution of syntactic communication is obtained,opening the way of a meaningful language between machines.