Towards Abstract Relational Learning in Human Robot Interaction
- URL: http://arxiv.org/abs/2011.10364v1
- Date: Fri, 20 Nov 2020 12:06:46 GMT
- Title: Towards Abstract Relational Learning in Human Robot Interaction
- Authors: Mohamadreza Faridghasemnia, Daniele Nardi, Alessandro Saffiotti
- Abstract summary: Humans have a rich representation of the entities in their environment.
If robots need to interact successfully with humans, they need to represent entities, attributes, and generalizations in a similar way.
In this work, we address the problem of how to obtain these representations through human-robot interaction.
- Score: 73.67226556788498
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Humans have a rich representation of the entities in their environment.
Entities are described by their attributes, and entities that share attributes
are often semantically related. For example, if two books have "Natural
Language Processing" as the value of their `title' attribute, we can expect
that their `topic' attribute will also be equal, namely, "NLP". Humans tend to
generalize such observations, and infer sufficient conditions under which the
`topic' attribute of any entity is "NLP". If robots need to interact
successfully with humans, they need to represent entities, attributes, and
generalizations in a similar way. This ends in a contextualized cognitive agent
that can adapt its understanding, where context provides sufficient conditions
for a correct understanding. In this work, we address the problem of how to
obtain these representations through human-robot interaction. We integrate
visual perception and natural language input to incrementally build a semantic
model of the world, and then use inductive reasoning to infer logical rules
that capture generic semantic relations, true in this model. These relations
can be used to enrich the human-robot interaction, to populate a knowledge base
with inferred facts, or to remove uncertainty in the robot's sensory inputs.
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