Commonsense knowledge (artificial intelligence)

Last updated

In artificial intelligence research, commonsense knowledge consists of facts about the everyday world, such as "Lemons are sour", or "Cows say moo", that all humans are expected to know. It is currently an unsolved problem in Artificial General Intelligence. The first AI program to address common sense knowledge was Advice Taker in 1959 by John McCarthy. [1]

Contents

Commonsense knowledge can underpin a commonsense reasoning process, to attempt inferences such as "You might bake a cake because you want people to eat the cake." A natural language processing process can be attached to the commonsense knowledge base to allow the knowledge base to attempt to answer questions about the world. [2] Common sense knowledge also helps to solve problems in the face of incomplete information. Using widely held beliefs about everyday objects, or common sense knowledge, AI systems make common sense assumptions or default assumptions about the unknown similar to the way people do. In an AI system or in English, this is expressed as "Normally P holds", "Usually P" or "Typically P so Assume P". For example, if we know the fact "Tweety is a bird", because we know the commonly held belief about birds, "typically birds fly," without knowing anything else about Tweety, we may reasonably assume the fact that "Tweety can fly." As more knowledge of the world is discovered or learned over time, the AI system can revise its assumptions about Tweety using a truth maintenance process. If we later learn that "Tweety is a penguin" then truth maintenance revises this assumption because we also know "penguins do not fly".

Commonsense reasoning

Commonsense reasoning simulates the human ability to use commonsense knowledge to make presumptions about the type and essence of ordinary situations they encounter every day, and to change their "minds" should new information come to light. This includes time, missing or incomplete information and cause and effect. The ability to explain cause and effect is an important aspect of explainable AI. Truth maintenance algorithms automatically provide an explanation facility because they create elaborate records of presumptions. Compared with humans, all existing computer programs that attempt human-level AI perform extremely poorly on modern "commonsense reasoning" benchmark tests such as the Winograd Schema Challenge. [3] The problem of attaining human-level competency at "commonsense knowledge" tasks is considered to probably be "AI complete" (that is, solving it would require the ability to synthesize a fully human-level intelligence), [4] [5] although some oppose this notion and believe compassionate intelligence is also required for human-level AI. [6] Common sense reasoning has been applied successfully in more limited domains such as natural language processing [7] [8] and automated diagnosis [9] or analysis. [10]

Commonsense knowledge base construction

Compiling comprehensive knowledge bases of commonsense assertions (CSKBs) is a long-standing challenge in AI research. From early expert-driven efforts like CYC and WordNet, significant advances were achieved via the crowdsourced OpenMind Commonsense project, which led to the crowdsourced ConceptNet KB. Several approaches have attempted to automate CSKB construction, most notably, via text mining (WebChild, Quasimodo, TransOMCS, Ascent), as well as harvesting these directly from pre-trained language models (AutoTOMIC). These resources are significantly larger than ConceptNet, though the automated construction mostly makes them of moderately lower quality. Challenges also remain on the representation of commonsense knowledge: Most CSKB projects follow a triple data model, which is not necessarily best suited for breaking more complex natural language assertions. A notable exception here is GenericsKB, which applies no further normalization to sentences, but retains them in full.

Applications

Around 2013, MIT researchers developed BullySpace, an extension of the commonsense knowledgebase ConceptNet, to catch taunting social media comments. BullySpace included over 200 semantic assertions based around stereotypes, to help the system infer that comments like "Put on a wig and lipstick and be who you really are" are more likely to be an insult if directed at a boy than a girl. [11] [12] [13]

ConceptNet has also been used by chatbots [14] and by computers that compose original fiction. [15] At Lawrence Livermore National Laboratory, common sense knowledge was used in an intelligent software agent to detect violations of a comprehensive nuclear test ban treaty. [16]

Data

As an example, as of 2012 ConceptNet includes these 21 language-independent relations: [17]

Commonsense knowledge bases

See also

Related Research Articles

<span class="mw-page-title-main">Cyc</span> Artificial intelligence project

Cyc is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works. Hoping to capture common sense knowledge, Cyc focuses on implicit knowledge. The project began in July 1984 at MCC and was developed later by the Cycorp company.

Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning.

Planner is a programming language designed by Carl Hewitt at MIT, and first published in 1969. First, subsets such as Micro-Planner and Pico-Planner were implemented, and then essentially the whole language was implemented as Popler by Julian Davies at the University of Edinburgh in the POP-2 programming language. Derivations such as QA4, Conniver, QLISP and Ether were important tools in artificial intelligence research in the 1970s, which influenced commercial developments such as Knowledge Engineering Environment (KEE) and Automated Reasoning Tool (ART).

In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology.

Natural language understanding (NLU) or natural language interpretation (NLI) is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem.

<span class="mw-page-title-main">Douglas Lenat</span> Computer scientist and AI pioneer

Douglas Bruce Lenat was an American computer scientist and researcher in artificial intelligence who was the founder and CEO of Cycorp, Inc. in Austin, Texas.

Open Mind Common Sense (OMCS) is an artificial intelligence project based at the Massachusetts Institute of Technology (MIT) Media Lab whose goal is to build and utilize a large commonsense knowledge base from the contributions of many thousands of people across the Web. It has been active from 1999 to 2016.

In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.

In the history of artificial intelligence, neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 1970s and was a subject of discussion until the mid-1980s.

Mindpixel was a web-based collaborative artificial intelligence project which aimed to create a knowledgebase of millions of human validated true/false statements, or probabilistic propositions. It ran from 2000 to 2005.

Reason maintenance is a knowledge representation approach to efficient handling of inferred information that is explicitly stored. Reason maintenance distinguishes between base facts, which can be defeated, and derived facts. As such it differs from belief revision which, in its basic form, assumes that all facts are equally important. Reason maintenance was originally developed as a technique for implementing problem solvers. It encompasses a variety of techniques that share a common architecture: two components—a reasoner and a reason maintenance system—communicate with each other via an interface. The reasoner uses the reason maintenance system to record its inferences and justifications of the inferences. The reasoner also informs the reason maintenance system which are the currently valid base facts (assumptions). The reason maintenance system uses the information to compute the truth value of the stored derived facts and to restore consistency if an inconsistency is derived.

In artificial intelligence (AI), commonsense reasoning is a human-like ability to make presumptions about the type and essence of ordinary situations humans encounter every day. These assumptions include judgments about the nature of physical objects, taxonomic properties, and peoples' intentions. A device that exhibits commonsense reasoning might be capable of drawing conclusions that are similar to humans' folk psychology and naive physics.

<span class="mw-page-title-main">History of artificial intelligence</span>

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The study of logic and formal reasoning from antiquity to the present led directly to the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.

The following outline is provided as an overview of and topical guide to artificial intelligence:

In philosophy, a process ontology refers to a universal model of the structure of the world as an ordered wholeness. Such ontologies are fundamental ontologies, in contrast to the so-called applied ontologies. Fundamental ontologies do not claim to be accessible to any empirical proof in itself but to be a structural design pattern, out of which empirical phenomena can be explained and put together consistently. Throughout Western history, the dominating fundamental ontology is the so-called substance theory. However, fundamental process ontologies have become more important in recent times, because the progress in the discovery of the foundations of physics has spurred the development of a basic concept able to integrate such boundary notions as "energy," "object", and those of the physical dimensions of space and time.

The Winograd schema challenge (WSC) is a test of machine intelligence proposed in 2012 by Hector Levesque, a computer scientist at the University of Toronto. Designed to be an improvement on the Turing test, it is a multiple-choice test that employs questions of a very specific structure: they are instances of what are called Winograd schemas, named after Terry Winograd, professor of computer science at Stanford University.

Luminoso is a Cambridge, MA-based text analytics and artificial intelligence company. It spun out of the MIT Media Lab and its crowd-sourced Open Mind Common Sense (OMCS) project.

<span class="mw-page-title-main">Catherine Havasi</span> American AI scientist

Catherine Havasi is an American scientist who specializes in artificial intelligence (AI) at MIT Media Lab. She co-founded and was CEO of AI company, Luminoso for 8 years. Havasi was a member of the MIT group engaged in the Open Mind Common Sense AI project that created the natural language AI program ConceptNet. Havasi is currently the Chief of Innovation and Technology Strategy at Babel Street, AI-enabled data-to-knowledge platform.

KnowRob is a system which combines knowledge representation and reasoning methods to acquire and ground knowledge. This system is the backbone of openEASE. both under developing at the Institute for Artificial Intelligence at the University of Bremen, Germany.

References

  1. ↑ "PROGRAMS WITH COMMON SENSE". www-formal.stanford.edu. Retrieved 2018-04-11.
  2. ↑ Liu, Hugo, and Push Singh. "ConceptNet—a practical commonsense reasoning tool-kit." BT technology journal 22.4 (2004): 211-226.
  3. ↑ "The Winograd Schema Challenge". cs.nyu.edu. Retrieved 9 January 2018.
  4. ↑ Yampolskiy, Roman V. " 10.1.1.232.913.pdf#page=102 AI-Complete, AI-Hard, or AI-Easy-Classification of Problems in AI AI-Easy-Classification of Problems in AI]." MAICS 2012.
  5. ↑ Andrich, C, Novosel, L, and Hrnkas, B. (2009). Common Sense Knowledge. Information Search and Retrieval, 2009.
  6. ↑ Mason, Cindy (2010-09-27). "The Logical Road to Human Level AI Leads to a Dead End". 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop. Vol. 32. pp. 57–95. doi:10.1109/SASOW.2010.63. ISBN   978-1-4244-8684-7. S2CID   13030524.
  7. ↑ Chutima, Boonthum-Denecke (2011-12-31). Cross-Disciplinary Advances in Applied Natural Language Processing: Issues and Approaches: Issues and Approaches. IGI Global. ISBN   978-1-61350-448-2.
  8. ↑ Davis, Ernest (2014-07-10). Representations of Commonsense Knowledge. Morgan Kaufmann. ISBN   978-1-4832-2113-7.
  9. ↑ Reiter, Raymond (1987-04-01). "A theory of diagnosis from first principles". Artificial Intelligence. 32 (1): 57–95. CiteSeerX   10.1.1.170.9236 . doi:10.1016/0004-3702(87)90062-2. ISSN   0004-3702. S2CID   15629917.
  10. ↑ Gallimore, R.J.; Jennings, N.R.; Lamba, H.S.; Mason, C.L.; Orenstein, B.J. (1999). "Cooperating agents for 3-D scientific data interpretation" (PDF). IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews. 29: 110–126. doi:10.1109/5326.740674.
  11. ↑ Bazelon, Emily (March 2013). "How to Stop the Bullies". The Atlantic. Retrieved 9 January 2018.
  12. ↑ Dinakar, Karthik; Jones, Birago; Havasi, Catherine; Lieberman, Henry; Picard, Rosalind (1 September 2012). "Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying". ACM Transactions on Interactive Intelligent Systems. 2 (3): 1–30. CiteSeerX   10.1.1.693.8065 . doi:10.1145/2362394.2362400. S2CID   5560081.
  13. ↑ "AI systems could fight cyberbullying". New Scientist. 27 June 2012. Retrieved 9 January 2018.
  14. ↑ "I Believe That It Will Become Perfectly Normal for People to Have Sex With Robots". Newsweek. 23 October 2014. Retrieved 9 January 2018.
  15. ↑ "Told by a robot: Fiction by storytelling computers". New Scientist. 24 October 2014. Retrieved 9 January 2018.
  16. ↑ Mason, C.L. (1995). "An intelligent assistant for nuclear test ban treaty verification". IEEE Expert. 10 (6): 42–49. doi:10.1109/64.483116.
  17. ↑ Speer, Robert, and Catherine Havasi. "Representing General Relational Knowledge in ConceptNet 5." LREC. 2012.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.