Abstract
Despite its proven relevance, ASP (answer set programming) suffers from a lack of transparency in its outputs. Much like other popular artificial intelligence systems such as deep learning, the results do not come with any explanation to support their derivation. In this paper, we use a given answer set as guidance for a simplified top-down procedure of answer set semantics developed by Satoh and Iwayama to provide not only an explanation for the derivation (or non-derivation) of the atoms, but also an explanation for the consistency of the whole answer set itself. Additionally, we show that a full use of the Satoh-Iwayama procedure gives an explanation of why an atom is not present in any answer set.
The work of Jérémie Dauphin was supported by the H2020 Marie Skłodowska-Curie grant number 690974 for the project MIREL.
K. Satoh—This work was partially supported by JSPS KAKENHI Grant Number 17H06103.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
In this example, \(q\,\leftarrow \, \sim \!\! p\) is the only rule for deriving q but even if there were other rules, we can identify this rule given M.
- 2.
We display 0 instead of \(\bot \) for notational consistency with the meta-interpreter output of the Satoh-Iwayama procedure.
- 3.
Since we know that \(q\in M\), we can speed up the process by skipping the other checks.
References
Cabalar, P., Fandinno, J., Fink, M.: Causal graph justifications of logic programs. Theory Pract. Logic Program. 14(4–5), 603–618 (2014)
Dowling, W.F., Gallier, J.: Linear-time algorithms for testing the satisfiability of propositional horn formulae. J. Logic Program. 1(3), 267–284 (1984)
Erdem, E., Gelfond, M., Leone, N.: Applications of answer set programming. AI Mag. 37(3), 53–68 (2016)
Fages, F.: A new fixpoint semantics for general logic programs compared with the well-founded and the stable model semantics. New Gener. Comput. 9(3–4), 425–443 (1991)
Fandinno, J., Schulz, C.: Answering the “why” in answer set programming - a survey of explanation approaches. Theory Pract. Logic Program. 19(2), 114–203 (2019)
Pontelli, E., Son, T.C., El-Khatib, O.: Justifications for logic programs under answer set semantics. Theory Pract. Logic Program. 9(1), 1–56 (2009)
Satoh, K., Iwayama, N.: A correct goal-directed proof procedure for a general logic program with integrity constraints. In: Lamma, E., Mello, P. (eds.) ELP 1992. LNCS, vol. 660, pp. 24–44. Springer, Heidelberg (1993). https://doi.org/10.1007/3-540-56454-3_2
Schulz, C., Toni, F.: Justifying answer sets using argumentation. Theory Pract. Logic Program. 16(1), 59–110 (2016)
Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artificial Intelligence (2018)
Lewis, D.: Causal explanation. Philos. Pap. 2, 214–240 (1986)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Dauphin, J., Satoh, K. (2019). Explainable ASP. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds) PRIMA 2019: Principles and Practice of Multi-Agent Systems. PRIMA 2019. Lecture Notes in Computer Science(), vol 11873. Springer, Cham. https://doi.org/10.1007/978-3-030-33792-6_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-33792-6_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33791-9
Online ISBN: 978-3-030-33792-6
eBook Packages: Computer ScienceComputer Science (R0)