On Concepts and Emergence of Language

The existing theories of concept formation involve categorization based upon the physical features that differentiate the concept. Physical features do not provide the understanding of objects, entities, events, or words, and so cannot be used to form a concept. We have come to believe that the affect the object, entity, event, or word has on the environment is what needs to be evaluated for true concept formation. Following our argument for a change in the direction of research, our views on some of the other aspects of concept formation are presented.

On a Robotic Platform for MASIVE-Like Experiments

This chapter overviews a robotic platform developed at our Cognitive Agency and Robotics Laboratory (CARoL, n.d.) for the purpose of carrying out Interactivist-Expectative Theory of Agency and Learning (IETAL) and Multi-Agent Simulated Interactive Virtual Environments (MASIVE)-like experiments in a realistic environment. Performing IETAL and MASIVE-like experiments with a robotic agent(s) requires specialized agent(s) in a specialized environment. The solution overviewed here is done on a shoestring budget and is easy to replicate and modify.

On Drives

In this chapter we briefly overview the connections between drives, motivations, and actions in an agent. Drives take a central place in our agents and influence significantly how they use their internalization of the environment. Appendix B focuses more elaborately on these phenomena in humans.

On Our Four Foci

In this chapter we give a brief overview of the history and the key developments of the concepts of agents and multi-agent systems that are relevant to the book, as preliminaries to the presentation of the theories in the subsequent chapters of the first section of the book.

On Future Work

This chapter concludes the book by stating open questions and directions for further research. Some of the topics that we focus on here are the interaction between the human and nonhuman agents, interactions of the human in heterogeneous environments, and alternative representations of environments. We also present Izbushka, a context-0 interactive environment, and discuss our immediate future research expectations from its implementation, as well as its implication to multiple application areas.

On an Evolutionary Approach to Language

The focus of this chapter is the explanation of a method for allowing languages to emerge within a multi-agent system. The need for such a method tends to be in larger multi-agent systems that focus either on large domains or span across multiple domains. This method can also be adapted for interfacing multi-agent systems with humans through natural languages. Also addressed in this chapter are the necessary requirements for a multi-agent system to utilize an evolving communication system. A specification of an evolving vocabulary is presented along with an explanation of results from an experiment that contains an implementation of these specifications.

On Emergent Phenomena

“Emergence” is itself emergent; although originating in the context of the “sciences of complexity” — i.e., life sciences, cybernetics, multiagent systems research, and artificial life research — “emergent thinking” has spread to other parts of the academy, including the social sciences and business. Utilizing examples drawn from popular culture, this chapter looks to the ways IT has proven influential in other cultural contexts, but not without a price. The second part of the chapter interrogates the transportation of emergent thinking into these other discourses, taking them to task for not embracing the promises inherent in emergence and, in fact, merely reproducing the old under the sign of the emergent new. Finally, by borrowing notions of “surprise” from robotics and multiagent systems, I suggest new possibilities for emergence to lead to genuine paradigm shifts in the ways we think.

On Learning

In this chapter we explain how Interactivist-Expectative Theory of Agency and Learning (IETAL) agents learn their environment and how they build their intrinsic, internal representation of it, which they then use to build their expectations when on quest to satisfy their active drives.

On IETAL, Algebraically

In this chapter, we formalize the Interactivist-Expectative Theory of Agency and Learning (IETAL) agent in an algebraic framework and focus on issues of learnability based on context.

Appendix B

What motivates people? Why do they behave as they do? For that matter, why do people do anything at all? Questions like these have persisted over 100 years of psychology despite decades upon decades of research to answer them. Waves of academic thinking have addressed these issues, with each new school of thought providing different answers, at least in form if not in function. In our brief overview of motivation theory, we will emphasize commonalities in theory rather than their differences. We will then discuss evolutionary theory as an overarching, modern model of motivation — specifically, the motivations to promote one’s survival and to be reproductively fit in a Darwinian sense. This discussion of evolutionary theory will be applied to human motivation and to the many ways in which people — such as ourselves — adapt in a complex world.