10 research outputs found
Computing Strong and Weak Permissions in Defeasible Logic
In this paper we propose an extension of Defeasible Logic to represent and
compute three concepts of defeasible permission. In particular, we discuss
different types of explicit permissive norms that work as exceptions to
opposite obligations. Moreover, we show how strong permissions can be
represented both with, and without introducing a new consequence relation for
inferring conclusions from explicit permissive norms. Finally, we illustrate
how a preference operator applicable to contrary-to-duty obligations can be
combined with a new operator representing ordered sequences of strong
permissions which derogate from prohibitions. The logical system is studied
from a computational standpoint and is shown to have liner computational
complexity
Free choice permission in defeasible deontic logic
Free Choice Permission is one of the challenges for the formalisation of norms. In this paper, we follow a novel approach that accepts Free Choice Permission in a restricted form. The intuition behind the guarded form is strongly aligned with the idea of defeasibility. Accordingly, we investigate how to model the guarded form in Defeasible Deontic Logic extended with disjunctive permissions
Sequence Semantics for Norms and Obligations
This paper presents a new version of the sequence semantics presented at DEON 2014. This new version allows us for a capturing the distinction between logic of obligations and logic of norms. Several axiom schemata are discussed, while soundness and completeness results are proved
Sequence Semantics for Modelling Reason-based Preferences
We study how the non-classical n-ary operator circle times, originally intended to capture the concept of reparative obligation, can be used in the context of social choice theory to model preferences. A novel possible-world model-theoretic semantics, called sequence semantics, was proposed for the operator. In this paper, we propose a sound and complete axiomatisation of a minimal modal logic for the operator, and we extend it with axioms suitable to model social choice consistency principles such as extension consistency and contraction consistency. We provide completeness results for such extensions
Large-Scale Legal Reasoning with Rules and Databases
Traditionally, computational knowledge representation and reasoning focused its attention on rich domains such as the law. The main underlying assumption of traditional legal knowledge representation and reasoning is that knowledge and data are both available in main memory. However, in the era of big data, where large amounts of data are generated daily, an increasing rangeof scientific disciplines, as well as business and human activities, are becoming data-driven. This chapter summarises existing research on legal representation and reasoning in order to uncover technical challenges associated both with the integration of rules and databases and with the main concepts of the big data landscape. We expect these challenges lead naturally to future research directions towards achieving large scale legal reasoning with rules and databases
Fine-Grained Access Control via Policy-Carrying Data
We address the problem of associating access policies with datasets and how to monitor compliance via policy-carrying data. Our contributions are a formal model in first-order logic inspired by normative multi-agent systems to regulate data access, and a computational model for the validation of specific use cases and the verification of policies against criteria. Existing work on access policy identifies roles as a key enabler, with which we concur, but much of the rest focusses on authentication and authorization technology. Our proposal aims to address the normative principles put forward in Berners-Lee’s bill of rights for the internet, through human-readable but machine-processable access control policies.</jats:p
On the computational complexity of ethics: moral tractability for minds and machines
Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis
Automated Deduction – CADE 28
This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions