In this book, Marcin Milkowski argues that the mind can be explained computationally because it is itself computational—whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music. Defending the computational explanation against objections to it—from John Searle and Hilary Putnam in particular—Milkowski writes that computationalism is here to stay but is not what many have taken it to be. It does not, for example, rely on a Cartesian gulf between software and hardware, or mind and brain.
Over a century ago, William James proposed that people search through memory much as they rummage through a house looking for lost keys. We scour our environments for territory, food, mates, and information. We search for items in visual scenes, for historical facts, and for the best deals on Internet sites; we search for new friends to add to our social networks, and for solutions to novel problems. What we find is always governed by how we search and by the structure of the environment.
The consideration of time or dynamics is fundamental for all aspects of mental activity--perception, cognition, and emotion--because the main feature of brain activity is the continuous change of the underlying brain states even in a constant environment. The application of nonlinear dynamics to the study of brain activity began to flourish in the 1990s when combined with empirical observations from modern morphological and physiological observations. This book offers perspectives on brain dynamics that draw on the latest advances in research in the field.
In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures.
When we play the ancient and noble game of chess, we grapple with ideas about honesty, deceitfulness, bravery, fear, aggression, beauty, and creativity, which echo (or allow us to depart from) the attitudes we take in our daily lives. Chess is an activity in which we deploy almost all our available cognitive resources; therefore, it makes an ideal laboratory for investigation into the workings of the mind. Indeed, research into artificial intelligence (AI) has used chess as a model for intelligent behavior since the 1950s.
Classical cognitive science has found itself in something of a pickle; a pickle that's so deep (if I may mix a metaphor) that most of its practitioners haven't so much as noticed that they are in it. What's so good about Pylyshyn—in particular what's so good about Pylyshyn's recent work—is that maybe, just possibly maybe, it shows us the way out of the pickle we're in.
In The Allure of Machinic Life, John Johnston examines new forms of nascent life that emerge through technical interactions within human-constructed environments—"machinic life"—in the sciences of cybernetics, artificial life, and artificial intelligence. With the development of such research initiatives as the evolution of digital organisms, computer immune systems, artificial protocells, evolutionary robotics, and swarm systems, Johnston argues, machinic life has achieved a complexity and autonomy worthy of study in its own right.
In Human Reasoning and Cognitive Science, Keith Stenning and Michiel van Lambalgen—a cognitive scientist and a logician—argue for the indispensability of modern mathematical logic to the study of human reasoning. Logic and cognition were once closely connected, they write, but were "divorced" in the past century; the psychology of deduction went from being central to the cognitive revolution to being the subject of widespread skepticism about whether human reasoning really happens outside the academy.
Constraint logic programming, the notion of computing with partial information, is becoming recognized as a way of dramatically improving on the current generation of programming languages. This collection presents the best of current work on all aspects of constraint logic programming languages, from theory through language implementation.
