A probabilistic deontic argumentation framework

Régis Riveret: Conceptualization, Formal analysis, Validation, Writing - original draft, Writing - review & editing. Nir Oren: Validation, Writing - original draft, Writing - review & editing. Giovanni Sartor: Conceptualization, Validation, Writing - original draft, Writing - review & editing.Peer reviewedPostprin

Time and defeasibility in FIPA ACL semantics

AbstractInferences about speech acts are often conditional, non-monotonic, and involve the issue of time. Most agent communication languages, however, ignore these issues, due to the difficulty to combine them in a single formalism. This paper addresses such issues in defeasible logic, and shows how to express a semantics for ACLs in order to make non-monotonic inferences on the basis of speech acts

Interactions between normative systems and software cognitive agents. A formalization in temporal modal defeasible logic and its implementation

Sustainable computer systems require some flexibility to adapt to environmental unpredictable changes. A solution lies in autonomous software agents which can adapt autonomously to their environments. Though autonomy allows agents to decide which behavior to adopt, a disadvantage is a lack of control, and as a side effect even untrustworthiness: we want to keep some control over such autonomous agents. How to control autonomous agents while respecting their autonomy? A solution is to regulate agents’ behavior by norms. The normative paradigm makes it possible to control autonomous agents while respecting their autonomy, limiting untrustworthiness and augmenting system compliance. It can also facilitate the design of the system, for example, by regulating the coordination among agents. However, an autonomous agent will follow norms or violate them in some conditions. What are the conditions in which a norm is binding upon an agent? While autonomy is regarded as the driving force behind the normative paradigm, cognitive agents provide a basis for modeling the bindingness of norms. In order to cope with the complexity of the modeling of cognitive agents and normative bindingness, we adopt an intentional stance. Since agents are embedded into a dynamic environment, things may not pass at the same instant. Accordingly, our cognitive model is extended to account for some temporal aspects. Special attention is given to the temporal peculiarities of the legal domain such as, among others, the time in force and the time in efficacy of provisions. Some types of normative modifications are also discussed in the framework. It is noteworthy that our temporal account of legal reasoning is integrated to our commonsense temporal account of cognition. As our intention is to build sustainable reasoning systems running unpredictable environment, we adopt a declarative representation of knowledge. A declarative representation of norms will make it easier to update their system representation, thus facilitating system maintenance; and to improve system transparency, thus easing system governance. Since agents are bounded and are embedded into unpredictable environments, and since conflicts may appear amongst mental states and norms, agent reasoning has to be defeasible, i.e. new pieces of information can invalidate formerly derivable conclusions. In this dissertation, our model is formalized into a non-monotonic logic, namely into a temporal modal defeasible logic, in order to account for the interactions between normative systems and software cognitive agents

On Learning Attacks in Probabilistic Abstract Argumentation

ABSTRACT Probabilistic argumentation combines the quantitative uncertainty accounted by probability theory with the qualitative uncertainty captured by argumentation. In this paper, we investigate the problem of learning the structure of an argumentative graph to account for (a distribution of) labellings of a set of arguments. We consider a general abstract framework, where the structure of arguments is left unspecified, and we focus on the grounded semantics. We present, with experimental insights, an anytime algorithm evaluating 'on the fly' hypothetical attacks from the examination of an input stream of labellings. Keywords Probabilistic Abstract Argumentation; Structure Learning

Probabilistic Rule-Based Argumentation for Norm-Governed Learning Agents

This paper proposes an approach to investigate norm-governed learning agents which combines a logic-based formalism with an equation-based counterpart. This dual formalism enables us to describe the reasoning of such agents and their interactions using argumentation, and, at the same time, to capture systemic features using equations. The approach is applied to norm emergence and internalisation in systems of learning agents. The logical formalism is rooted into a probabilistic defeasible logic instantiating Dung’s argumentation framework. Rules of this logic are attached with probabilities to describe the agents’ minds and behaviours as well as uncertain environments. Then, the equation-based model for reinforcement learning, defined over this probability distribution, allows agents to adapt to their environment and self-organise

Legal Consolidation formalised in Defeasible Logic and based on Agents.

Abstract. Updated legal corpora have been indicated by the European Union as fundamental to eDemocracy, and member states looking to set up eGovernment initiatives are acting on that input. However, the usual automation of legal consolidation presents shortcomings, namely, the collapse of temporal dimensions and local views of normative systems. This paper presents solutions to these shortcomings by providing the formalisation in logic of an appropriate legal temporal model and an investigation of the use of the multi-agent paradigm

Evaluation of logic-based smart contracts for blockchain systems

Date: 28 June 2016While procedural languages are commonly used to program smart contracts in blockchain systems, logic-based languages may be interesting alternatives. In this paper, we inspect what are the possible legal and technical (dis)advantages of logic-based smart contracts in light of common activities featuring ordinary contracts, then we provide insights on how to use such logic-based smart contracts in combination with blockchain systems. These insights lead us to emphasize a fundamental challenge - algorithms for logic approaches have to be efficient, but they also need to be literally cheap as measured within the environment where they are deployed and according to its economic rules. We illustrate this with different algorithms from defeasible logic-based frameworks

Heuristics in Argumentation: A Game-Theoretical Investigation

This paper provides a game-theoretical investigation on how to determine optimal strategies in dialogue games for argumentation. To make our ideas as widely applicable as possible, we adopt an abstract dialectical setting and model dialogues as extensive games with perfect information where optimal strategies ar