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Principle-based non-monotonic reasoning - from humans to machines
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Interactive and Intelligent Systems Group)ORCID iD: 0000-0002-6458-2252
2022 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Principbaserat icke-monotoniskt resonemang - från människor till maskiner (Swedish)
Abstract [en]

A key challenge when developing intelligent agents is to instill behavior into computing systems that can be considered as intelligent from a common-sense perspective. Such behavior requires agents to diverge from typical decision-making algorithms that strive to maximize simple and often one-dimensional metrics. A striking parallel to this research problemcan be found in the design of formal models of human decision-making in microeconomic theory. Traditionally, mathematical models of human decision-making also reflect the ambition to maximize expected utility or a preference function, which economists refer to as the rational man paradigm. However, evidence suggests that these models are flawed, not only because human decision-making is subject to systematic fallacies, but also because the models depend on assumptions that do not hold in reality. Consequently, the research domain of formally modeling bounded rationality emerged, which attempts to account for these shortcomings by systematically relaxing the mathematical constraints of the formal model of economic rationality. Similarly, in the field of symbolic reasoning, approaches have emerged to systematically relax the notion of monotony of entailment, which stipulates (colloquially speaking) that when inferring a set of statements from a knowledge base, the addition of new knowledge to the knowledge base must not lead to the rejection of any of the previously inferred statements.

By drawing from these developments in microeconomic theory and symbolic reasoning, this thesis explores different principle-based approaches to decision-making and non-monotonic reasoning. Thereby, abstract argumentation is used as a fundamental method for reasoning in face of conflicting knowledge (or: beliefs) that reduces non-monotonic reasoning to the problem of drawing conclusions (extensions) from a directed graph, and hence provides a neat abstraction for theoretical exploration. In particular, the works collected in this thesis i) introduce the consistent preferences property of microeconomic theory, as well as some relaxed forms of monotony of entailment as mathematical principles to abstract argumentation-based inference; ii) show how to enforce some of these principles in dynamic environments; iii) devise a formal approach to maximize monotony of entailment, given the constraints imposed by an inference function; iv) extend and apply the aforementioned approaches to the domains of machine reasoning explainability and legal reasoning.

Place, publisher, year, edition, pages
Umeå: Umeå University , 2022. , p. 34
Series
Report / UMINF, ISSN 0348-0542 ; 22.02
Keywords [en]
Non-monotonic reasoning, formal argumentation
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-193460ISBN: 978-91-7855-757-8 (print)ISBN: 978-91-7855-758-5 (electronic)OAI: oai:DiVA.org:umu-193460DiVA, id: diva2:1649097
Public defence
2022-04-29, MA121 (MIT-huset), Umeå University, Umeå, 13:15 (English)
Opponent
Supervisors
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

Digital ISBN missing in publication. 

Available from: 2022-04-08 Created: 2022-04-02 Last updated: 2022-04-04Bibliographically approved
List of papers
1. Explainable Reasoning in Face of Contradictions: From Humans to Machines
Open this publication in new window or tab >>Explainable Reasoning in Face of Contradictions: From Humans to Machines
2021 (English)In: Explainable and Transparent AI and Multi-Agent Systems: Third International Workshop, EXTRAAMAS 2021, Virtual Event, May 3–7, 2021, Revised Selected Papers / [ed] Davide Calvaresi, Amro Najjar, Michael Winikoff, Kary Främling, Cham: Springer, 2021, p. 280-295Conference paper, Published paper (Refereed)
Abstract [en]

A well-studied trait of human reasoning and decision-making is the ability to not only make decisions in the presence of contradictions, but also to explain why a decision was made, in particular if a decision deviates from what is expected by an inquirer who requests the explanation. In this paper, we examine this phenomenon, which has been extensively explored by behavioral economics research, from the perspective of symbolic artificial intelligence. In particular, we introduce four levels of intelligent reasoning in face of contradictions, which we motivate from a microeconomics and behavioral economics perspective. We relate these principles to symbolic reasoning approaches, using abstract argumentation as an exemplary method. This allows us to ground the four levels in a body of related previous and ongoing research, which we use as a point of departure for outlining future research directions.

Place, publisher, year, edition, pages
Cham: Springer, 2021
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12688
Keywords
Symbolic artificial intelligence, Explainable artificial intelligence, Non-monotonic reasoning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-186267 (URN)10.1007/978-3-030-82017-6_17 (DOI)000691781800017 ()2-s2.0-85113339576 (Scopus ID)978-3-030-82017-6 (ISBN)978-3-030-82016-9 (ISBN)
Conference
3rd International Workshop on Explainable, Transparent AI and Multi-Agent Systems, EXTRAAMAS 2021, Virtual, Online, May 3-7, 2021
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 12688)

Available from: 2021-07-20 Created: 2021-07-20 Last updated: 2023-09-05Bibliographically approved
2. Abstract argumentation and the rational man
Open this publication in new window or tab >>Abstract argumentation and the rational man
2021 (English)In: Journal of logic and computation (Print), ISSN 0955-792X, E-ISSN 1465-363X, Vol. 31, no 2, p. 654-699Article in journal (Refereed) Published
Abstract [en]

Abstract argumentation has emerged as a method for non-monotonic reasoning that has gained popularity in the symbolic artificial intelligence community. In the literature, the different approaches to abstract argumentation that were refined over the years are typically evaluated from a formal logics perspective; an analysis that is based on models of economically rational decision-making does not exist. In this paper, we work towards addressing this issue by analysing abstract argumentation from the perspective of the rational man paradigm in microeconomic theory. To assess under which conditions abstract argumentation-based decision-making can be considered economically rational, we derive reference independence as a non-monotonic inference property from a formal model of economic rationality and create a new argumentation principle that ensures compliance with this property. We then compare the reference independence principle with other reasoning principles, in particular with cautious monotony and rational monotony. We show that the argumentation semantics as proposed in Dung’s seminal paper, as well as other semantics we evaluate, with the exception of naive semantics and the SCC-recursive CF2 semantics, violate the reference independence principle. Consequently, we investigate how structural properties of argumentation frameworks impact the reference independence principle and identify cyclic expansions (both even and odd cycles) as the root of the problem. Finally, we put reference independence into the context of preference-based argumentation and show that for this argumentation variant, which explicitly models preferences, reference independence cannot be ensured in a straight-forward manner.

Place, publisher, year, edition, pages
Oxford University Press, 2021
Keywords
formal argumentation, economic rationality, non-monotonic reasoning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-179618 (URN)10.1093/logcom/exab003 (DOI)000637297400012 ()2-s2.0-85104727094 (Scopus ID)
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2021-02-04 Created: 2021-02-04 Last updated: 2023-09-05Bibliographically approved
3. Ensuring reference independence and cautious monotony in abstract argumentation
Open this publication in new window or tab >>Ensuring reference independence and cautious monotony in abstract argumentation
2022 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 140, p. 173-210Article in journal (Refereed) Published
Abstract [en]

In the symbolic artificial intelligence community, abstract argumentation with its semantics, i.e. approaches for defining sets of valid conclusions (extensions) that can be derived from argumentation graphs, is considered a promising method for non-monotonic reasoning. However, from a sequential perspective, abstract argumentation-based decision-making processes typically do not guarantee an alignment with common formal notions to assess consistency; in particular, abstract argumentation can, in itself, not enforce the satisfaction of relational principles such as reference independence (based on a key principle of microeconomic theory) and cautious monotony. In this paper, we address this issue by introducing different approaches to ensuring reference independence and cautious monotony in sequential argumentation: a reductionist, an expansionist, and an extension-selecting approach. The first two approaches are generically applicable, but may require comprehensive changes to the corresponding argumentation framework. In contrast, the latter approach guarantees that an extension of the corresponding argumentation framework can be selected to satisfy the relational principle by requiring that the used argumentation semantics is weakly reference independent or weakly cautiously monotonous, respectively, and also satisfies some additional straightforward principles. To highlight the relevance of the approach, we illustrate how the extension-selecting approach to reference independent argumentation can be applied to model (boundedly) rational economic decision-making.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Applied Mathematics, Artificial Intelligence, Theoretical Computer Science, Software
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-189163 (URN)10.1016/j.ijar.2021.10.007 (DOI)000721007500003 ()2-s2.0-85117800047 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationWallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2021-11-06 Created: 2021-11-06 Last updated: 2023-09-05Bibliographically approved
4. The Degrees of Monotony-Dilemma in Abstract Argumentation
Open this publication in new window or tab >>The Degrees of Monotony-Dilemma in Abstract Argumentation
2021 (English)In: Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 16th European Conference, ECSQARU 2021, Prague, Czech Republic, September 21–24, 2021, Proceedings / [ed] Jiřina Vejnarová, Nic Wilson, Cham: Springer, 2021, p. 89-102Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we introduce the notion of the degree of monotony to abstract argumentation, a well-established method for drawing inferences in face of conflicts in non-monotonic reasoning. Roughly speaking, the degree of monotony allows us, given an abstract argumentation semantics and an abstract argumentation framework to be as monotonic as possible, when iteratively drawing inferences and expanding the argumentation framework. However, we also show that when expanding an argumentation framework several times using so-called normal expansions, an agent may, at any given step, select a conclusion that has the highest degree of monotony w.r.t. the previous conclusion (considering the constraints of the semantics), but end up with a conclusion that has a suboptimal degree of monotony w.r.t. one or several conclusions that precede the previous conclusion. We formalize this observation as the degrees of monotony-dilemma.

Place, publisher, year, edition, pages
Cham: Springer, 2021
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 12897
Keywords
abstract argumentation, non-monotonic reasoning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-187845 (URN)10.1007/978-3-030-86772-0_7 (DOI)000711926000007 ()2-s2.0-85116445896 (Scopus ID)978-3-030-86772-0 (ISBN)978-3-030-86771-3 (ISBN)
Conference
16th European Conference, ECSQARU 2021, Prague, Czech Republic, September 21–24, 2021
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 12897)

Available from: 2021-09-22 Created: 2021-09-22 Last updated: 2023-09-05Bibliographically approved
5. Explanations of Non-monotonic Inference in Admissibility-Based Abstract Argumentation
Open this publication in new window or tab >>Explanations of Non-monotonic Inference in Admissibility-Based Abstract Argumentation
2021 (English)In: Logic and Argumentation: 4th International Conference, CLAR 2021, Hangzhou, China, October 20–22, 2021, Proceedings / [ed] Pietro Baroni, Christoph Benzmüller and Yὶ N. Wang, Cham: Springer, 2021, p. 209-223Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we introduce a formal framework for explaining change of inference in abstract argumentation, in particular in the context of iteratively drawing inferences from a sequence of normal expansions, with a focus on admissible set-based semantics. We then conduct a formal analysis, showing that given an initial argumentation framework and an extension that has been inferred from it, we can guarantee the existence of explanation arguments for the violation of monotony when inferring an extension from a normal expansion of the initial argumentation framework.

Place, publisher, year, edition, pages
Cham: Springer, 2021
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 13040
Keywords
Formal argumentation, Explainable artificial intelligence, Non-monotonic reasoning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-188626 (URN)10.1007/978-3-030-89391-0_12 (DOI)000754548600012 ()2-s2.0-85118111656 (Scopus ID)978-3-030-89391-0 (ISBN)978-3-030-89390-3 (ISBN)
Conference
CLAR 2021 - Fourth International Conference on Logic and Argumentation, Hybrid (virtual and physical), via Hangzhou, China, October 20-22, 2021
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2021-10-16 Created: 2021-10-16 Last updated: 2023-09-05Bibliographically approved
6. The Burden of Persuasion in Abstract Argumentation
Open this publication in new window or tab >>The Burden of Persuasion in Abstract Argumentation
2021 (English)In: Logic and Argumentation: 4th International Conference, CLAR 2021, Hangzhou, China, October 20–22, 2021, Proceedings / [ed] Pietro Baroni, Christoph Benzmüller and Yὶ N. Wang, Springer, 2021, p. 224-243Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we provide a formal framework for modeling the burden of persuasion in legal reasoning. The framework is based on abstract argumentation, a frequently studied method of non-monotonic reasoning, and can be applied to different argumentation semantics; it supports burdens of persuasion with arbitrary many levels, and allows for the placement of a burden of persuasion on any subset of an argumentation framework's arguments. Our framework can be considered an extension of related works that raise questions on how burdens of persuasion should be handled in some conflict scenarios that can be modeled with abstract argumentation. An open source software implementation of the introduced formal notions is available as an extension of an argumentation reasoning library.

Place, publisher, year, edition, pages
Springer, 2021
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 13040
Keywords
Formal argumentation, Non-monotonic reasoning, Legal reasoning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-188625 (URN)10.1007/978-3-030-89391-0_13 (DOI)000754548600013 ()2-s2.0-85118142754 (Scopus ID)978-3-030-89391-0 (ISBN)978-3-030-89390-3 (ISBN)
Conference
CLAR 2021 - Fourth International Conference on Logic and Argumentation, Hybrid (virtual and physical), via Hangzhou, China, October 20-22, 2021
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2021-10-16 Created: 2021-10-16 Last updated: 2023-09-05Bibliographically approved
7. Argumentation-based Health Information Systems: A Design Methodology
Open this publication in new window or tab >>Argumentation-based Health Information Systems: A Design Methodology
Show others...
2021 (English)In: IEEE Intelligent Systems, ISSN 1541-1672, E-ISSN 1941-1294, Vol. 36, no 2, p. 72-80Article in journal (Refereed) Published
Abstract [en]

In this article, we present a design methodology for argumentation-based health information systems. With a focus on the application of formal argumentation, the methodology aims at eliciting requirements in regard to argumentation reasoning behavior, knowledge and user models, and business logic on levels below and above the argumentation layer. We highlight specific considerations that need to be made dependent on the system type, i.e., for clinical decision-support systems, patient-facing systems, and administration systems. In addition, we outline challenges in regards to the design of argumentation-based intelligent systems for healthcare, considering the state of the art of argumentation research, health information systems, and software design methods. For each challenge, we outline a mitigation strategy. 

Place, publisher, year, edition, pages
Los Alamitos, CA, USA: IEEE Computer Society, 2021
Keywords
Formal argumentation, healthcare, software design
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-179620 (URN)10.1109/MIS.2020.3044944 (DOI)000654783900009 ()2-s2.0-85098787449 (Scopus ID)
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)EU, Horizon 2020
Available from: 2021-02-04 Created: 2021-02-04 Last updated: 2023-03-24Bibliographically approved

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