Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[10] By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.[11]
Many of these algorithms are insufficient for solving large reasoning problems because they experience a “combinatorial explosion”: they became exponentially slower as the problems grew larger.[12] 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.[13] Accurate and efficient reasoning is an unsolved problem.
Knowledge representation
An ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.
Knowledge representation and knowledge engineering[14] allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval,[15] scene interpretation,[16] clinical decision support,[17] knowledge discovery (mining “interesting” and actionable inferences from large databases),[18] and other areas.[19]
A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge.[20] Knowledge bases need to represent things such as: objects, properties, categories and relations between objects; [21] situations, events, states and time;[22] causes and effects;[23] knowledge about knowledge (what we know about what other people know);[24] default reasoning (things that humans assume are true until they are told differently and will remain true even when other facts are changing);[25] and many other aspects and domains of knowledge.
Among the most difficult problems in KR are: the breadth of commonsense knowledge (the set of atomic facts that the average person knows is enormous);[26] and the sub-symbolic form of most commonsense knowledge (much of what people know is not represented as “facts” or “statements” that they could express verbally).[13]
Knowledge acquisition is the difficult problem of obtaining knowledge for AI applications.[c] Modern AI gathers knowledge by “scraping” the internet (including Wikipedia). The knowledge itself was collected by the volunteers and professionals who published the information (who may or may not have agreed to provide their work to AI companies).[29] This “crowd sourced” technique does not guarantee that the knowledge is correct or reliable. The knowledge of Large Language Models (such as ChatGPT) is highly unreliable — it generates misinformation and falsehoods (known as “hallucinations”). Providing accurate knowledge for these modern AI applications is an unsolved problem.