Fluent (artificial intelligence)

<h2 id="naive-physics">Naive physics</h2>
<p>From a historical point of view, fluents were introduced in the context of qualitative reasoning. The idea is to describe a process model not with mathematical equations but with natural language. That means an action is not only determined by its trajectory, but with a symbolic model, very similar to a text adventure. Naive physics stands in opposition to a numerical physics engine and has the obligation to predict the outcome of actions.<a class="footnote-ref" id="fnref:1" href="#fn:1"><sup>1</sup></a> The fluent realizes the common sense grounding between the robot's motion and the task description in natural language.<a class="footnote-ref" id="fnref:2" href="#fn:2"><sup>2</sup></a>
</p><p>From a technical perspective, a fluent is equal to a parameter that is parsed by the naive physics engine. The parser converts between natural language fluents and numerical values measured by sensors.<a class="footnote-ref" id="fnref:3" href="#fn:3"><sup>3</sup></a> As a consequence, the human-machine interaction is improved.
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<h2 id="see-also">See also</h2>
<ul><li><a href="/facts/Event_calculus/82A2yW5H">Event calculus</a></li>
<li><a href="/facts/Fluent_calculus/Ieuy6qR4">Fluent calculus</a></li>
<li><a href="/facts/Frame_problem/fc1FMlzH">Frame problem</a></li>
<li><a href="/facts/Situation_calculus/dGcIavPa">Situation calculus</a></li></ul>

<h2 id="references">References</h2>

<ol>
<li id="fn:1"><p>L. Kunze and M. E. Dolha and M. Beetz (2011). Logic programming with simulation-based temporal projection for everyday robot object manipulation. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE. CiteSeerX 10.1.1.391.7309. doi:10.1109/iros.2011.6094743. <a href="/wiki/CiteSeerX_(identifier)" target="_blank">/wiki/CiteSeerX_(identifier)</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li>
<li id="fn:2"><p>Jakob Suchan and Mehul Bhatt (2017). Commonsense Scene Semantics for Cognitive Robotics: Towards Grounding Embodied Visuo-Locomotive Interactions. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). IEEE. arXiv:1709.05293. doi:10.1109/iccvw.2017.93. <a href="/wiki/ArXiv_(identifier)" target="_blank">/wiki/ArXiv_(identifier)</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li>
<li id="fn:3"><p>Caiming Xiong and Nishant Shukla and Wenlong Xiong and Song-Chun Zhu (2016). Robot learning with a spatial, temporal, and causal and-or graph. 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE. doi:10.1109/icra.2016.7487364. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li>
</ol>

Fluent (artificial intelligence) open-in-new

Fluent (artificial intelligence)