Abstract
We are interested in the differences between how a human agent and a logic-based software agent interpret a text in natural language. When reading a narrative, the human agent has a single interpretation model. That is the preferred model among the models consistent with the available information. The model is gradually adjusted as the story proceeds. Differently, a logic-based software agent works with a finite set of many models, in the same time. Of most interest is that the number of these models is huge, even for simple narratives. We compare here the reduction strategies of humans and software agents to keep the discourse more intelligible and tractable. One the one hand, the human agent extensively uses common knowledge, contextual reasoning and closes the world as much as possible. On the other hand, the logical agent adds domain knowledge (such as ontologies) and applied reduction strategies (such as identifying isomorphisms). The differences are analyse with puzzles in First order logic, Description logic and Dynamic epistemic logic.
This research is part-funded by the ExNanoMat-21PFE grant.
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Notes
- 1.
We assume the reader is familiar with the Description Logic syntax. Otherwise, the reader is referred to [1].
- 2.
For one implementation of this puzzle, the interested reader is referred to SMCDEL symbolic model checker for Dynamic Epistemic Logic (https://github.com/jrclogic/SMCDEL)Â [2].
- 3.
- 4.
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Groza, A. (2020). On the Differences Between Human Agents and Logic-Based Software Agents Discourse Understanding. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_1
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