George E. Mobus

Foraging search: Prototypical intelligence

We think because we eat. Or as Descartes might have said, on a little more reflection, “I need to eat, therefore I think.” Animals that forage for a living repeatedly face the problem of searching for a sparsely distributed resource in a vast space. Furthermore, the resource may occur sporadically and episodically under conditions of true uncertainty (nonstationary, complex and non-linear dynamics). I assert that this problem is the canonical problem solved by intelligence. It’s solution is the basis for the evolution of more advanced intelligence in which the space of search includes that of concepts (objects and relations) encoded in cortical structures. In humans the conscious experience of searching through concept space we call thinking. The foraging search model is based upon a higher-order autopoeitic system (the forager) employing anticipatory processing to enhance its success at finding food while avoiding becoming food or having accidents in a hostile world. I present a semi-formal description o...

Development of a Smart Building Wireless Sensors Network: Cooperation between University of Washington Tacoma and Brazilian universities

The Institute of Technology at the University of Washington Tacoma - UWT is expanding its international cooperation by hosting Brazilian undergraduate and graduate students in the Computer Engineering and Systems program. In the first research project originated from this cooperation we propose a generic and scalable system, based on Wireless Sensor Networks (WSN). Using the data acquired from the sensor nodes this system will be able to take simple decisions, such as turn on/off heater and lights and help in other more complex decisions, such as rearranging rooms based on the occupancy. These actions aim to save resources and make buildings more comfortable and efficient. In this paper we describe how this research project is being structured and conducted in order to maximize the cooperation between Brazilian and UWT researchers. Also, we show which strategies are being adopted to make the project scalable and generic. This will allow us to aggregate multi-disciplinary people and make the knowledge and technology produced to be reusable by future project members.

Conditioned response training of robots using Adaptrode-based neural networks

Summary form only given, as follows. A simple competitive learning network of Adaptrode-based artificial neurons has been shown to learn a conditioned response behavior. The response characteristics are a function of frequency, duration, phase, and intensity of copresentations of a conditionable stimulus with an unconditionable stimulus. Furthermore, the system is capable of encoding a short-term, contrary association without interference with the long-term association. Mobile, autonomous systems may be trained by conditioned response to perform desired behaviors in remote locations while maintaining an ability to learn from experience.<<ETX>>

Auto-Organization and Emergence

This chapter introduces and explores the fundamental organizing principles of systems. Up until now, we have been describing how systems are, how they are organized, how they work, etc. Starting in this chapter, we will be examining the general processes that account for how systems, particularly complex ones, actually come into existence, how they get to be how they are. Underlying the development of complex systems are the twin processes of auto-organization and emergence. These, in turn, are part of an overarching process, that of evolution, which will be covered in the subsequent chapter. Auto-organization explains how components of systems first start to organize and interoperate. Emergence explains how functions come into existence at a level of organization built upon what new structures have auto-organized and have survived within their environments. Perhaps the paradigm example of these processes at work is the origin of life.

Information, Meaning, Knowledge, and Communications

The physical world is understood to be comprised of matter and energy, but in the early twentieth century, science began to recognize the significance of something seemingly less physical and yet at the heart of the organization and functioning of systems. Information was defined and characterized scientifically and is now recognized as a fundamental aspect of the universe that is neither matter nor energy per se. We provide an overview of that scientific viewpoint and relate the nature of information to other nearby concepts such as how it is communicated, how is it related to meaning, and most importantly, how is it related to knowledge. Information and knowledge are, in a sense, inverses of one another, alluded to by Morowitz’s quote above. These ephemeral elements are critical in the coming chapters where we see how they contribute to what makes complex systems work.

Behavior: System Dynamics

Systems are never still. Even a rock weathers and often changes chemically over long enough time scales. Systems are dynamic, which means they have behavior. In this chapter we explore the dynamic properties of systems from a number of perspectives. Systems as a whole behave in their environments. But systems contain active components that also behave internally relative to one another. We look at a myriad of characteristics of system dynamics to understand this important principle. A key concept that pertains to system dynamics is that of energy flow and work. Every physical change involves the accomplishment of work, which requires the use of energy. The laws of thermodynamics come into play in a central way in systems science.

Cybernetics: The Role of Information and Computation in Systems

Information, as defined in Chap. 7, and computation, as described in Chap. 8, will now be put to work in systems. Cybernetics is the science of control and regulation as it applies to maintaining the functions of systems. In this chapter, we investigate basic cybernetics and control theory. But we also investigate how complex systems have complex regulatory subsystems that, unsurprisingly, form a hierarchy of specialized control or management functions. These subsystems process information for the purpose of managing the material processes within the system and to coordinate the system’s behaviors with respect to its environment. This chapter covers what might be argued to be the most crucial aspect of the science of complex systems such as biological and social systems. It is the longest!

Systems Principles in the Real World: Understanding Drug-Resistant TB

An example of how a complex modern problem for humankind can be considered in terms of systems science should help in understanding how the principles introduced in Chap. 1 can be applied. Drug-resistant tuberculosis has become a threat brought on by our very use of antibiotics and the power of evolution to select more fit bacteria strain—fit that is to not be affected adversely by antibiotics originally developed by humans to kill them and prevent disease. This chapter lays out the complexity of the problem and examines its facets through the lenses of the principles.

Networks: Connections Within and Without

A key attribute of systems is that internally the components are connected in various relations. That is, the physical system is a network of relations between components. It is also possible to “represent” a system as an abstract network of nodes and links. The science and mathematics of networks can be brought to bear on the analysis of these representations, and characteristics of network topologies can be used to help understand structures, functions, and overall dynamics.

Lessons Learned from MAVRIC’s Brain: An Anticipatory Artificial Agent and Proto‐consciousness

MAVRIC II is a mobile, autonomous robot whose brain is comprised almost entirely of artificial adaptrode‐based neurons. These neurons were previously shown to encode anticipatory actions. The architecture of this brain is based on the Extended Braitenberg Architecture (EBA). We are still in the process of collecting hard data on the behavioral traits of MAVRIC in the generalized foraging search task. But even now sufficient qualitative aspects of MAVRIC’s behavior have been garnered from foraging experiments to lend strong support to the theory that MAVRIC is a highly adaptive, life‐like agent. The development of the current MAVRIC brain has led to some important insights into the nature of intelligent control. In this paper we elucidate some of these principles in the form of lessons learned, and project the potential for future developments.