Editing Artificial intelligence (section)

=== Reasoning and problem-solving ===
Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical [[Deductive reasoning|deductions]].<ref>Problem-solving, puzzle solving, game playing, and deduction: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 3–5}}, {{Harvtxt|Russell|Norvig|2021|loc=chpt. 6}} ([[constraint satisfaction]]), {{Harvtxt|Poole|Mackworth|Goebel|1998|loc=chpt. 2, 3, 7, 9}}, {{Harvtxt|Luger|Stubblefield|2004|loc=chpt. 3, 4, 6, 8}}, {{Harvtxt|Nilsson|1998|loc=chpt. 7–12}}</ref> By the late 1980s and 1990s, methods were developed for dealing with [[uncertainty|uncertain]] or incomplete information, employing concepts from [[probability]] and [[economics]].<ref>Uncertain reasoning: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 12–18}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=345–395}}, {{Harvtxt|Luger|Stubblefield|2004|pp=333–381}}, {{Harvtxt|Nilsson|1998|loc=chpt. 7–12}}</ref>

Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": They become exponentially slower as the problems grow.<ref name="Intractability and efficiency and the combinatorial explosion">[[Intractably|Intractability and efficiency]] and the [[combinatorial explosion]]: {{Harvtxt|Russell|Norvig|2021|p=21}}</ref> Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.<ref name="Psychological evidence of the prevalence of sub">Psychological evidence of the prevalence of sub-symbolic reasoning and knowledge: {{Harvtxt|Kahneman|2011}}, {{Harvtxt|Dreyfus|Dreyfus|1986}}, {{Harvtxt|Wason|Shapiro|1966}}, {{Harvtxt|Kahneman|Slovic|Tversky|1982}}</ref> Accurate and efficient reasoning is an unsolved problem.