Darwin's Rainbow: Evolutionary radiation and the spectrum of consciousness

Evolution is littered with paraphyletic convergences: many roads lead to functional Romes. We propose here another example - an equivalence class structure factoring the broad realm of possible realizations of the Baars Global Workspace consciousness model. The construction suggests many different physiological systems can support rapidly shifting, sometimes highly tunable, temporary assemblages of interacting unconscious cognitive modules. The discovery implies various animal taxa exhibiting behaviors we broadly recognize as conscious are, in fact, simply expressing different forms of the same underlying phenomenon. Mathematically, we find much slower, and even multiple simultaneous, versions of the basic structure can operate over very long timescales, a kind of paraconsciousness often ascribed to group phenomena. The variety of possibilities, a veritable rainbow, suggests minds today may be only a small surviving fraction of ancient evolutionary radiations - bush phylogenies of consciousness and paraconsciousness. Under this scenario, the resulting diversity was subsequently pruned by selection and chance extinction. Though few traces of the radiation may be found in the direct fossil record, exaptations and vestiges are scattered across the living mind. Humans, for instance, display an uncommonly profound synergism between individual consciousness and their embedding cultural heritages, enabling efficient Lamarkian adaptation

Inductive Synthesis of Cover-Grammars with the Help of Ant Colony Optimization

A cover-grammar of a finite language is a context-free grammar that accepts all words in the language and possibly other words that are longer than any word in the language. In this paper, we describe an efficient algorithm aided by Ant Colony System that, for a given finite language, synthesizes (constructs) a small cover-grammar of the language. We also check its ability to solve a grammatical inference task through the series of experiments

A Machine Made of Words: Our Incompletely Theorized Constitution

[Excerpt]”Many scholars have observed that the Constitution of the United States can be understood as an example of what Cass Sunstein calls an “incompletely theorized agreement.” The Constitution contains a number of extremely general terms, such as “liberty,” “necessary and proper,” and “due process.” The Framers of the Constitution, it is suggested, did not attempt to specify precisely how each of these principles would operate in every case. On this view, the Constitution is incompletely theorized in the sense of representing “a comfortable and even emphatic agreement on a general principle, accompanied by sharp disagreement about particular cases.” For example, the Framers presumably could have agreed on the value of liberty in the abstract but disagreed sharply on its application to slaves. There is, however, another sense in which the Constitution can be seen as an incompletely theorized agreement. This second sense has received less attention in the existing scholarship, perhaps because it appears to conflict with the first. According to this sense, the Constitution is remarkable for containing so little theory and so few statements of general principle. What the interpretations of the Constitution in the previous paragraph take to be statements of general principle are, on closer inspection, almost never merely statements of principle. Outside of its errata and signatures, the Constitution of 1787 consists of only two elements: the single, performative sentence of the Preamble and the series of commands and permissions that make up the body of the document. Neither of these elements offers abstract, theoretical statements of general principle. On the one hand, the performative Preamble is not, strictly speaking, a descriptive statement at all; it is a performative enactment of the will of “the People” ratifying the Constitution at conventions across the thirteen states. On the other hand, every clause in the body of the document, without exception, is constructed around either a “shall,” a “may,” or a “shall not.” In grammatical form and function, these clauses are not theoretical justifications or elaborations. They are highly pragmatic directives.

La Salle College Bulletin Summer Sessions 1970

Issued for La Salle College Summer 1970https://digitalcommons.lasalle.edu/course_catalogs/1088/thumbnail.jp

Some Questions Inspired by (Membrane Computing Motivated) Language-Theoretic Models

This contribution argues for the proposition that formal models based on the theory of formal grammars and languages are adequate for the study of some computationally relevant properties of agents and multi-agent systems. Some questions are formulated concerning the possibilities to enlarge the universality and realism of such models by considering the possibilities to go with their computing abilities beyond the traditional Turing-computability, and by considering very natural properties of any real (multi-)agent system such as the partially predictable functioning (behavior) of agents, their unreliability, dysfunctions, etc

Toward evolutionary and developmental intelligence

Given the phenomenal advances in artificial intelligence in specific domains like visual object recognition and game playing by deep learning, expectations are rising for building artificial general intelligence (AGI) that can flexibly find solutions in unknown task domains. One approach to AGI is to set up a variety of tasks and design AI agents that perform well in many of them, including those the agent faces for the first time. One caveat for such an approach is that the best performing agent may be just a collection of domain-specific AI agents switched for a given domain. Here we propose an alternative approach of focusing on the process of acquisition of intelligence through active interactions in an environment. We call this approach evolutionary and developmental intelligence (EDI). We first review the current status of artificial intelligence, brain-inspired computing and developmental robotics and define the conceptual framework of EDI. We then explore how we can integrate advances in neuroscience, machine learning, and robotics to construct EDI systems and how building such systems can help us understand animal and human intelligence