Online research in philosophy

Herbert Simon’s work presents a curious anomaly to the historian and philosopher trying to understand the development of classic Artificial Intelligence (AI). Simon was one of most influential figures in AI since its birth, and yet it is always with some difficulties that his work can be made to fit within the received canon of AI’s development and goals. In fact, he differed from every other figure in early AI on most counts: in terms of the recognized intellectual heritage of AI, of his own background and training, of the goals he set for his own AI work and the assessment criteria he accepted. I will argue that these differences provide important clues toward a reevaluation of the relationship between Artificial Intelligence and Herbert Simon’s work that may change our current understanding of both. On the one hand, classic Artificial Intelligence (or Complex Information Processing, as Simon preferred to call it for a number of years), provided the tool he needed to pursue a much broader research agenda that strove to encompass human beings in their cognitive, emotional, social, and political dimensions. On the other hand, AI’s curious status as the discipline that straggles the boundaries between engineering, science, and philosophy can be recast as the tool that allowed researchers to pursue philosophy’s old goals with an alternative methodology. From this perspective, Simon’s version of Artificial Intelligence becomes a full-fledged form of ”anti-philosophy” as ambitious and broad-ranging as old-fashioned metaphysics and as revolutionary as the latter in the radical refashioning of its methodology. It follows that a philosophical assessment of AI, at in least in its Simonian incarnation, must be more farreaching than it is usually thought. At the methodological level, it must discuss whether AI’s invention of computer simulation as the tool that overcomes the a-priori/a-posteriori distinction by actually producing the behavior it wants to explain is really adequate to the job at hand..

Artificial life attempts to understand the essential general properties of living systems by synthesizing life-like behavior in software, hardware and biochemicals. As many of the essential abstract properties of living systems (e.g. autonomous adaptive and intelligent behavior) are also studied by cognitive science, artificial life and cognitive science have an essential overlap. This review highlights the state of the art in artificial life with respect to dynamical hierarchies, molecular selforganization, evolutionary robotics, the evolution of complexity and language, and other practical applications. It also speculates about future connections between artificial life and cognitive science.

Mike Anderson1 has given us a thoughtful and useful field guide: Not in the genre of a bird-watcher’s guide which is carried in the field and which contains detailed descriptions of possible sightings, but in the sense of a guide to a field (in this case embodied cognition) which aims to identify that field’s general principles and properties. I’d like to make some comments that will hopefully complement Anderson’s work, highlighting points of agreement and disagreement between his view of the field and my own, and acting as a devil’s advocate in places where further discussion seems to be required. Given the venue for this guide, we can safely restrict the discussion to embodied artificial intelligence (EAI), even if such work draws on notions of embodied cognition..

This paper revisits some foundational questions concerning the abstract representation of a discourse context. The context of a conversation is represented by a body of information that is presumed to be shared by the participants in the conversation – the information that the speaker presupposes a point at which a speech act is interpreted. This notion is designed to represent both the information on which context-dependent speech acts depend, and the situation that speech acts are designed to affect, and so to be a representation of context that is appropriate for explaining the interaction of context and the contents expressed in them. After reviewing the motivating ideas and the outlines of the apparatus, the paper responds to a criticism of the framework, and considers the way it can help to clarify some phenomena concerning pronouns with indefinite antecedents.

An application of Narrative Knowledge Representation Language (NKRL) techniques on (declassified) ‘terrorism in Southern Philippines’ documents has been carried out in the context of the IST Parmenides project. This paper describes some aspects of this work: it is our belief, in fact, that the Knowledge Representation techniques and the Intelligent Information Retrieval tools used in this experiment can be of some interest also in an ‘Ontological Modelling of Legal Events and Legal Reasoning’ context.

The need for a coherent answer to this question has become increasingly urgent in the past few years, particularly in the field of artificial intelligence. There, both logical and probabilistic techniques are routinely applied in an attempt to solve complex problems such as parsing natural language and determining the way proteins fold. The hope is that some combination of logic and probability will produce better solutions. After all, both natural language and protein molecules have some structure that admits logical representation and reasoning; yet inherent uncertainties also demand the use of probabilistic methods: this structure is only partially known and does not in any case fully determine a solution—context or environment also play a role.

replicated by artificial intelligence (AI). The firstpersonal, subjective, what-it-is-like-to-be-something nature of consciousness is thought to be untouchable by the computations, algorithms, processing and functions of AI method. Since AI is the most promising avenue toward artificial consciousness (AC), the conclusion many draw is that AC is..

In this essay we advance the view that analytical epistemology and artificial intelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificial intelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificial intelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relation.

The notion of context arises in assorted areas of artificial intelligence (AI), including knowledge representation, natural language processing, intelligent information retrieval, etc. Although the term ‘context’ is frequently employed in descriptions, explanations, and analyses of computer programs in these areas, its meaning is frequently left to the reader’s understanding. In other words, it is used in an intuitive manner. In an influential paper, Clark and Carlson (1981) state that context has become a favourite word. They then complain that the denotation of the word has become murkier as its uses have been extended in many directions, making context some sort of ‘conceptual garbage can.’.

sights which such projects will exhibit may be used as a general direction for further Artificial Life implementations. The purpose of such a model is to demonstrate by way of simulation how higher cognitive structures may emerge from building invariants by simple sensorimotor beings. By using the bottom-up methodology of Artificial Life, it is hoped to overcome problems that arise from dealing with complex systems, such as the phenomenon of cognition. The research will lead to both epistemological and technical implications. The proposed ALife model is intended to point out the usefulness of an interdisciplinary approach including methodological approaches from disciplines such as Artificial Intelligence, Cognitive Science, Theoretical Biology, and Artificial Life. I try to put them in one single context. The epistemological background which is necessary for this purpose comes from the ideas developed in both epistemological and psychological Constructivism. The model differs from other ALife approaches— and is somewhat radical in this sense—as it tries to start on the lowest possible level, i.e. avoids several a priori assumptions and anthropocentric ascriptions. Due to this characterization, the project may be alternatively viewed as testing the complementary relationship between epistemology and methodology.