Computational tradeoffs under bounded resources

Over the nearly fifty years of research in Artificial Intelligence, investigators have continued to highlight the computational hardness of implementing core competencies associated with intelligence. Key pillars of AI, including search, constraint propagation, belief updating, learning, decision making, and the associated real-world challenges of planning, perception, natural language understanding, speech recognition, and automated conversation continue to make salient the omnipresent wall of computational hardness. Early pioneers in AI research, including Alan Newell and Herbert Simon, established a long tradition of battling obvious intractabilities by resorting to approximations that relied on heuristic procedures—informal policies that appeared to perform acceptably on subsets of real-world problems. Bounded rationality was conceived and popularized in the context of sample applications that relied on such heuristic procedures to struggle through overwhelming complexity.