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Commonsense knowledge (artificial intelligence)
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<h2 id="commonsense-reasoning">Commonsense reasoning</h2> <p class="note">Main article: <a href="/facts/Commonsense_reasoning/HfL7zQe4">Commonsense reasoning</a></p> <p>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 <a href="/facts/Explainable_artificial_intelligence/dS566nb2">explainable AI</a>. <a href="/facts/Reason_maintenance/9UNXvlo9">Truth maintenance</a> algorithms automatically provide an explanation facility because they create elaborate records of presumptions. Compared with humans, all existing computer programs that attempt <a href="/facts/Artificial_general_intelligence/vbxwaq8t">human-level AI</a> perform extremely poorly on modern "commonsense reasoning" benchmark tests such as the <a href="/facts/Winograd_Schema_Challenge/CJjSVGb3">Winograd Schema Challenge</a>.<a class="footnote-ref" id="fnref:3" href="#fn:3"><sup>3</sup></a> The problem of attaining human-level competency at "commonsense knowledge" tasks is considered to probably be "<a href="/facts/AI_complete/Ik4mAVRy">AI complete</a>" (that is, solving it would require the ability to synthesize a fully human-level intelligence),<a class="footnote-ref" id="fnref:4" href="#fn:4"><sup>4</sup></a><a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a> although some oppose this notion and believe compassionate intelligence is also required for human-level AI.<a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a> Common sense reasoning has been applied successfully in more limited domains such as <a href="/facts/Natural_language_processing/1hjMKsSN">natural language processing</a><a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a><a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a> and automated diagnosis<a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a> or analysis.<a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a> </p> <h2 id="commonsense-knowledge-base-construction">Commonsense knowledge base construction</h2> <p>Compiling comprehensive knowledge bases of commonsense assertions (CSKBs) is a long-standing challenge in AI research. From early expert-driven efforts like CYC and <a href="/facts/WordNet/TbrGVGAF">WordNet</a>, significant advances were achieved via the crowdsourced <a href="/facts/Open_Mind_Common_Sense/H6ok2o1h">OpenMind Commonsense</a> 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. </p> <h2 id="applications">Applications</h2> <p>Around 2013, <a href="/facts/MIT/Xk49vNA8">MIT</a> researchers developed BullySpace, an extension of the commonsense knowledgebase <a href="/facts/ConceptNet/H6ok2o1h">ConceptNet</a>, 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.<a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a><a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a><a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a> </p><p>ConceptNet has also been used by chatbots<a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a> and by computers that compose original fiction.<a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a> At <a href="/facts/Lawrence_Livermore_National_Laboratory/WuXIbIKz">Lawrence Livermore National Laboratory</a>, common sense knowledge was used in an <a href="/facts/Software_agent/aevgKHF0">intelligent software agent</a> to detect violations of a <a href="/facts/Comprehensive_Nuclear-Test-Ban_Treaty/PJukufKN">comprehensive nuclear test ban</a> treaty.<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a> </p> <h2 id="data">Data</h2> <p>As an example, as of 2012 ConceptNet includes these 21 language-independent relations:<a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a> </p> <ul><li><a href="/facts/Is-a/cNm5WPlr">IsA</a> (An "<a href="/facts/Recreational_vehicle/8VHhzPRl">RV</a>" is a "vehicle")</li> <li>UsedFor</li> <li><a href="/facts/Has-a/lyFhMcdw">HasA</a> (A "<a href="/facts/Rabbit/8jQGWcQD">rabbit</a>" has a "<a href="/facts/Tail/RXACP06e">tail</a>")</li> <li>CapableOf</li> <li>Desires</li> <li>CreatedBy ("<a href="/facts/Cake/jwUP0Bca">cake</a>" can be created by "<a href="/facts/Baking/SU8Cb6Wa">baking</a>")</li> <li>PartOf</li> <li>Causes</li> <li>LocatedNear</li> <li>AtLocation (Somewhere a "<a href="/facts/Cook_(profession)/y96yFYpC">Cook</a>" can be at a "<a href="/facts/Restaurant/UHmMUjln">restaurant</a>")</li> <li>DefinedAs</li> <li>SymbolOf (<i>X</i> represents <i>Y</i>)</li> <li>ReceivesAction ("cake" can be "<a href="/facts/Eating/Vtf2gffc">eaten</a>")</li> <li>HasPrerequisite (<i>X</i> cannot do <i>Y</i> unless <i>A</i> does <i>B</i>)</li> <li>MotivatedByGoal (You would "bake" because you want to "eat")</li> <li>CausesDesire ("baking" makes you want to "follow <a href="/facts/Recipe/ath1TWwf">recipe</a>")</li> <li>MadeOf</li> <li>HasFirstSubevent (The first thing required when you're doing <i>X</i> is for entity <i>Y</i> to do <i>Z</i>)</li> <li>HasSubevent ("eat" has subevent "swallow")</li> <li>HasLastSubevent</li></ul> <h2 id="commonsense-knowledge-bases">Commonsense knowledge bases</h2> <ul><li><a href="/facts/Cyc/JyTurpR6">Cyc</a></li> <li><a href="/facts/Open_Mind_Common_Sense/H6ok2o1h">Open Mind Common Sense</a> (data source) and <a href="/facts/ConceptNet/H6ok2o1h">ConceptNet</a> (datastore and NLP engine)</li> <li><a href="/facts/Evi_(software)/opSPjHLg">Evi</a></li> <li><a href="/facts/Graphiq/EuxkPx8n">Graphiq</a></li></ul> <h2 id="see-also">See also</h2> <ul><li><a href="/facts/Common_sense/TlPpRoAx">Common sense</a></li> <li><a href="/facts/Linked_data/XKhE5aTQ">Linked data</a> and the <a href="/facts/Semantic_Web/NFHo9dB7">Semantic Web</a></li> <li><a href="/facts/Reason_maintenance/9UNXvlo9">Truth Maintenance or Reason Maintenance</a></li> <li><a href="/facts/Ontology_(information_science)/SQlhl6lp">Ontology</a></li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>"PROGRAMS WITH COMMON SENSE". www-formal.stanford.edu. Retrieved 2018-04-11. <a href="http://www-formal.stanford.edu/jmc/mcc59/mcc59.html" target="_blank">http://www-formal.stanford.edu/jmc/mcc59/mcc59.html</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Liu, Hugo, and Push Singh. "ConceptNet—a practical commonsense reasoning tool-kit." BT technology journal 22.4 (2004): 211-226. <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>"The Winograd Schema Challenge". cs.nyu.edu. Retrieved 9 January 2018. <a href="https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html" target="_blank">https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>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. <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>Andrich, C, Novosel, L, and Hrnkas, B. (2009). Common Sense Knowledge. Information Search and Retrieval, 2009. <a href="https://web.archive.org/web/20180109181028/https://pdfs.semanticscholar.org/59e3/495f4f1e70629e17ddbbce06834626b2a363.pdf" target="_blank">https://web.archive.org/web/20180109181028/https://pdfs.semanticscholar.org/59e3/495f4f1e70629e17ddbbce06834626b2a363.pdf</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>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. <a href="978-1-4244-8684-7" target="_blank">978-1-4244-8684-7</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>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. <a href="978-1-61350-448-2" target="_blank">978-1-61350-448-2</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>Davis, Ernest (2014-07-10). Representations of Commonsense Knowledge. Morgan Kaufmann. ISBN 978-1-4832-2113-7. <a href="978-1-4832-2113-7" target="_blank">978-1-4832-2113-7</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> <li id="fn:9"><p>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. <a href="/wiki/CiteSeerX_(identifier)" target="_blank">/wiki/CiteSeerX_(identifier)</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></p></li> <li id="fn:10"><p>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. <a href="https://eprints.soton.ac.uk/252119/1/smc99.pdf" target="_blank">https://eprints.soton.ac.uk/252119/1/smc99.pdf</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></p></li> <li id="fn:11"><p>Bazelon, Emily (March 2013). "How to Stop the Bullies". The Atlantic. Retrieved 9 January 2018. <a href="https://www.theatlantic.com/magazine/archive/2013/03/how-to-stop-bullies/309217/" target="_blank">https://www.theatlantic.com/magazine/archive/2013/03/how-to-stop-bullies/309217/</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></p></li> <li id="fn:12"><p>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. <a href="/wiki/CiteSeerX_(identifier)" target="_blank">/wiki/CiteSeerX_(identifier)</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></p></li> <li id="fn:13"><p>"AI systems could fight cyberbullying". New Scientist. 27 June 2012. Retrieved 9 January 2018. <a href="https://www.newscientist.com/article/mg21428713-400-ai-systems-could-fight-cyberbullying/" target="_blank">https://www.newscientist.com/article/mg21428713-400-ai-systems-could-fight-cyberbullying/</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></p></li> <li id="fn:14"><p>"I Believe That It Will Become Perfectly Normal for People to Have Sex With Robots". Newsweek. 23 October 2014. Retrieved 9 January 2018. <a href="http://www.newsweek.com/2014/10/31/sex-robots-278791.html" target="_blank">http://www.newsweek.com/2014/10/31/sex-robots-278791.html</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></p></li> <li id="fn:15"><p>"Told by a robot: Fiction by storytelling computers". New Scientist. 24 October 2014. Retrieved 9 January 2018. <a href="https://www.newscientist.com/article/dn26377-told-by-a-robot-fiction-by-storytelling-computers/" target="_blank">https://www.newscientist.com/article/dn26377-told-by-a-robot-fiction-by-storytelling-computers/</a> <a href="#fnref:15" class="footnote-back-ref">↩</a></p></li> <li id="fn:16"><p>Mason, C.L. (1995). "An intelligent assistant for nuclear test ban treaty verification". IEEE Expert. 10 (6): 42–49. doi:10.1109/64.483116. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:16" class="footnote-back-ref">↩</a></p></li> <li id="fn:17"><p>Speer, Robert, and Catherine Havasi. "Representing General Relational Knowledge in ConceptNet 5." LREC. 2012. <a href="http://lrec-conf.org/proceedings/lrec2012/pdf/1072_Paper.pdf" target="_blank">http://lrec-conf.org/proceedings/lrec2012/pdf/1072_Paper.pdf</a> <a href="#fnref:17" class="footnote-back-ref">↩</a></p></li> </ol>