Exploring the Dynamics of Research Programs in Artificial Intelligence

The relevance of artificial intelligence (AI) research is underscored by its significance at the forefront of informatics and cognitive sciences. The unique nature of AI encompasses a wide array of scientific knowledge, engineering techniques, psychological models, and philosophical ideas. This dissertation aims to develop a methodological framework for analyzing the historical dynamics of AI research.

This section outlines the methodological challenges in analyzing AI as a scientific discipline. It discusses the dual nature of AI research, where findings can be viewed as both scientific knowledge and engineering projects. The study proposes a modified methodology based on the research programs of I. Lakatos, emphasizing the need for a nuanced understanding of AI's evolution.

The historical evolution of AI is divided into four key phases: the emergence of research programs in the 1950s, the expansion of academic interest in the 1960s, the establishment of AI as a scientific discipline in the 1970s, and the commercialization of AI technologies in the 1980s. Each phase highlights critical moments and shifts in the focus of AI research.

This section examines the reflexive nature of the scientific community involved in AI research. It discusses how scientists analyze their own work and the implications of the philosophical question, 'Can machines think?' on their methodologies and research directions.

The dissertation concludes by emphasizing the importance of a comprehensive methodological framework for understanding the historical dynamics of AI research. It highlights the potential for this framework to inform future studies and the ongoing evolution of AI as a scientific discipline.