"The authors of Machines Like Us explore what it would take to endow computers with the kind of common sense that humans depend on every day--critically needed for AI systems to be successful in the world and to become trustworthy"-- Provided by publisher
What can artificial intelligence teach us about the mind? If AI's underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It's a timely question. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example. In this book, Hector Levesque shifts the conversation to good old fashioned artificial intelligence, which is based not on heaps of data but on understanding commonsense intelligence. This kind of artificial intelligence is equipped to handle situations that depart from previous patterns - as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there's no more soy milk.
At the core of symbolic artificial intelligence, known as GOFAI, lies the concept of a knowledge base. This system emphasizes the importance of structured information and reasoning capabilities, allowing for complex problem-solving and decision-making. The book explores the architecture, methodologies, and applications of knowledge-based systems, illustrating their role in advancing AI. It also delves into challenges faced in the development and implementation of these systems, providing insights into their potential and future directions in the field of artificial intelligence.
Students explore the idea that thinking is a form of computation by learning to write simple computer programs for tasks that require thought.
It is a pleasure and an honor to be able to present this collection of papers to Ray Reiter on the occasion of his 60th birthday. To say that Ray's research has had a deep impact on the field of Artificial Intel ligence is a considerable understatement. Better to say that anyone thinking of do ing work in areas like deductive databases, default reasoning, diagnosis, reasoning about action, and others should realize that they are likely to end up proving corol laries to Ray's theorems. Sometimes studying related work makes us think harder about the way we approach a problem; studying Ray's work is as likely to make us want to drop our way of doing things and take up his. This is because more than a mere visionary, Ray has always been a true leader. He shows us how to proceed not by pointing from his armchair, but by blazing a trail himself, setting up camp, and waiting for the rest of us to arrive. The International Joint Conference on Ar tificial Intelligence clearly recognized this and awarded Ray its highest honor, the Research Excellence award in 1993, before it had even finished acknowledging all the founders of the field. The papers collected here sample from many of the areas where Ray has done pi oneering work. One of his earliest areas of application was databases, and this is re flected in the chapters by Bertossi et at. and the survey chapter by Minker.