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Auflistung Künstliche Intelligenz 38(1-2) - August 2024 nach Erscheinungsdatum
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- ZeitschriftenartikelAn ASP Implementation of Defeasible Deontic Logic(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Governatori, GuidoWe present a novel implementation of Defeasible Deontic Logic as an Answer Set Programming meta-program, and we evaluate the performance of the implementation against a recent set of benchmarks.
- ZeitschriftenartikelComputer-Verified Foundations of Metaphysics(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Kirchner, Daniel
- ZeitschriftenartikelNon-Classical Reasoning for Contemporary AI Applications(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Steen, Alexander; Benzmüller, Christoph
- ZeitschriftenartikelChallenges for Non-Classical Reasoning in Contemporary AI Applications(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Steen, Alexander; Benzmüller, ChristophIn knowledge representation and reasoning, a key area in artificial intelligence research, non-classical logics play a prominent double role: firstly, non-classical logic languages allow for a precise and transparent encoding of domain specific knowledge. Secondly, as the logical languages are equipped with custom-tailored rules of logical inference, they make available a principled approach to derive new knowledge from previous information. In practice, the first aspect addresses data storage and retrieval, the second aspect the utilization of available information. This article briefly surveys contemporary challenges of NCL research in AI.
- ZeitschriftenartikelModeling $$\mathscr {C}^{0}$$ C 0 Family Logics for Artificial Intelligence: Doxastic-Temporal Logics for Reasoning About Goals(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Oswald, James T.; Rozek, Brandon; Ferguson, Thomas M.We introduce the $$\mathscr {C}^{0}$$ C 0 family of logics, which include temporalized modal operators for belief and hyperintensional modal operators for obligations and goals. We motivate the $$\mathscr {C}^{0}$$ C 0 family as extended doxastic fragments of the $$\mathcal {DCEC}$$ DCEC family of logics, which are cognitive calculi designed for theory-of-mind reasoning among multiple artificial agents. In the literature, $$\mathcal {DCEC}$$ DCEC family logics are defined exclusively using proof-theoretic semantics. In this work we provide a model theory for the $$\mathscr {C}^{0}$$ C 0 family of logics which constitutes the first steps towards providing a model theory for the $$\mathcal {DCEC}$$ DCEC cognitive calculi family as a whole. We investigate the fragment relationships between both the $$\mathscr {C}^{0}$$ C 0 family and the $$\mathcal {DCEC}$$ DCEC family, produce a model theory for the $$\mathscr {C}^{0}$$ C 0 family and prove important results establishing completeness for all $$\mathscr {C}^{0}$$ C 0 family logics and establish soundness for $$\mathscr {C}^{0}$$ C 0 fragments without time.
- ZeitschriftenartikelLearning Normative Behaviour Through Automated Theorem Proving(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Neufeld, Emery A.Reinforcement learning (RL) is a powerful tool for teaching agents goal-directed behaviour in stochastic environments, and many proposed applications involve adopting societal roles which have ethical, legal, or social norms attached to them. Though multiple approaches exist for teaching RL agents norm-compliant behaviour, there are limitations on what normative systems they can accommodate. In this paper we analyse and improve the techniques proposed for use with the Normative Supervisor (Neufeld, et al., 2021)—a module which uses conclusions gleaned from a defeasible deontic logic theorem prover to restrict the behaviour of RL agents. First, we propose a supplementary technique we call violation counting to broaden the range of normative systems we can learn from, thus covering normative conflicts and contrary-to-duty norms. Additionally, we propose an algorithm for constructing a “normative filter”, a function that can be used to implement the addressed techniques without requiring the theorem prover to be run at each step during training or operation, significantly decreasing the overall computational overhead of using the normative supervisor. In order to demonstrate these contributions, we use a computer game-based case study, and thereafter discuss remaining problems to be solved in the conclusion.
- ZeitschriftenartikelTowards a Logical Foundation of Randomized Computation: Doctoral Thesis Abstract(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Antonelli, MelissaInteractions between logic and theoretical computer science are multiple and profound. In the last decades, they have been deeply investigated, but, surprisingly, the study of probabilistic computation was only marginally touched by such fruitful interchanges. The overall goal of my doctoral thesis was precisely that of start bridging this gap by developing logical systems corresponding to specific aspects of randomized computation and, due to them, by generalizing standard achievements to the probabilistic realm. To do so, the key ingredient is the introduction of new, measure-sensitive quantifiers associated with quantitative interpretations.
- ZeitschriftenartikelEye of the Beholder(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Richter, Kai-Florian
- ZeitschriftenartikelCLKR: Conditional Logic and Knowledge Representation(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Beierle, Christoph; Haldimann, Jonas; Schwarzer, LeonCLKR (Conditional Logic and Knowledge Representation) is an online repository of conditional logic resources for knowledge representation and reasoning. The question which entailments should follow from a conditional knowledge base consisting of a set of conditionals “ If A then usually B “ is central in logic-based AI. In order to support the practical side of this question, CLKR provides various collections of conditional knowledge bases and related resources. All knowledge bases available in CLKR can be processed directly with a corresponding reasoning system like InfOCF-Web. The sets of knowledge bases include examples as they are used in the literature for illustration, application knowledge bases from different domains, and systematically generated knowledge bases for evaluating implementations of nonmonotonic reasoning. A main emphasis of the current version of CLKR is on providing collections of knowledge bases in various normal forms that have been proposed for conditional knowledge bases, e.g., conditional normal form, antecedent normal form, and renaming normal form.
- ZeitschriftenartikelSpectra: An Expressive STRIPS-Inspired AI Planner Based on Automated Reasoning(KI - Künstliche Intelligenz: Vol. 38, No. 0, 2024) Rozek, Brandon; Bringsjord, SelmerResearch in automated planning traditionally focuses on model-based approaches that often sacrifice expressivity for computational efficiency. For artificial agents that operate in complex environments, however, frequently the agent needs to reason about the beliefs of other agents and be capable of handling uncertainty. We present Spectra, a STRIPS-inspired AI planner built atop automated reasoning. Our system is expressive, in that we allow for state spaces to be defined as arbitrary formulae. Spectra is also designed to be logic-agnostic, as long as an automated reasoner exists that can perform entailment and question-answering over it. Spectra can handle environments of unbounded uncertainty; and with certain non-classical logics, our system can create plans under epistemic beliefs. We highlight all of these features using the cognitive calculus $$\mathcal {DCC}$$ DCC . Lastly, we discuss that under this framework, in order to fully plan under uncertainty, a defeasible (= non-monotonic) logic can be used in conjunction with our planner.