The chapter discusses the hybrid connectionist models, which incorporate both connectionist and symbolic processing methods. The connectionist revolution has spurred vigorous theoretical debates about the nature of cognition and the various approaches toward understanding. It looks in particular at the following types of learning: supervised learning, unsupervised learning, and reinforcement learning. The chapter addresses the issue of symbolic processing in connectionist models. It gives a brief review of major paradigms of learning in neural networks. This chapter reviews briefly the history of connectionist models, identifying major ideas and major areas of applications. Research into artificial intelligence is thriving as never before, and promises continuing contributions, both practical to engineering and theoretical to science. Flourishing recent trends include soft computing, AI for data mining, agent-based AI, cognitive computing (including developmental robotics and artificial general intelligence), and the application of AI in cognitive science.

The chapter provides a concise account of the research interests in each of these subfields.

There are almost a dozen distinct subfields of AI research, each with its own specialized journals, conferences, workshops, and so on. These include knowledge-based expert systems, chess players, theorem provers, natural language processing, and a new killer application. It gives a brief history of AI with an account of some of its relatively recent major accomplishments. This chapter introduces the field of artificial intelligence (AI) through a review of its core themes, history, major research areas, and current trends.

Bostrom book