Recent studies have focused on autonomous agents that select their own goals and then select actions to achieve these goals, using online Goal Reasoning (GR). GR agents can revise goals and plans at execution time if unexpected outcomes occur. However, for ethical or legal agent design, even the partial execution of an online plan may result in foreseeable norm violations. To prevent these violations, it is crucial to incorporate GR already at the planning phase. To this end, we design an offline GR system that can harbour normative systems or deontic logics for goal generation. Our main results include a characterization and comparison of the completeness classes for a variety of offline GR planners, and a discussion of the irreducibility of offline GR to pure planning methods.

Abstract Default logic and formal argumentation are paradigmatic methods in the study of nonmonotonic inference. Defeasible information often comes in different strengths stemming from different degrees of reliability in epistemic applications or from varying strengths of authorities issuing norms in deontic applications. In both paradigms, methods have been developed to deal with prioritized knowledge bases and normative systems. Questions of comparability of these methods therefore naturally arise. Argumentation theory has been developed with a strong emphasis on unification. It is therefore a desideratum to obtain natural representations of various approaches to (prioritized) default logic within frameworks of structured argumentation, such as ASPIC. Important steps in this direction have been presented in Liao et al. (2016, 2018). In this work, we identify and address some problems in earlier translations, we broaden the focus from total to modular orderings of defaults and we consider non-normal defaults.

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and interacts with end users. All of these actors are stakeholders affected by the behavior of the autonomous system. We address the challenge of how the ethical views of such stakeholders can be integrated in the behavior of an autonomous system. We propose an ethical recommendation component called Jiminy which uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. A Jiminy represents the ethical views of each stakeholder by using normative systems, and has three ways of resolving moral dilemmas that involve the opinions of the stakeholders. First, the Jiminy considers how the arguments of the stakeholders relate to one another, which may already resolve the dilemma. Secondly, the Jiminy combines the normative systems of the stakeholders such that the combined expertise of the stakeholders may resolve the dilemma. Thirdly, and only if these two other methods have failed, the Jiminy uses context-sensitive rules to decide which of the stakeholders take preference over the others. At the abstract level, these three methods are characterized by adding arguments, adding attacks between arguments, and revising attacks between arguments. We show how a Jiminy can be used not only for ethical reasoning and collaborative decision-making, but also to provide explanations about ethical behavior.

In this article, we study a dialogue-based approach to multi-agent collaborative plan search in the framework of t-DeLP, an extension of DeLP for defeasible temporal reasoning. In t-DeLP programs, temporal facts and rules combine into arguments, which compare against each other to decide which of their conclusions are to prevail. By adding temporal actions for multiple agents to this argumentative logic programming framework, one obtains a centralized planning framework. In this planning system, it can be shown that breadth-first search is sound and complete for both forward and backward planning. The main contribution is to extend these results in centralized planning to cooperative planning tasks, where the executing agents themselves are assumed to have reasoning and planning abilities. In particular, we propose a planning algorithm where agents exchange information on plans using suitable dialogues. We show that the soundness and completeness properties of centralized t-DeLP plan search are preserved, so the dialoguing agents will reach an agreement upon a joint plan if and only if some solution exists.