Artificial agents will need to be aware of human moral and social norms, and able to use them in decision-making. In particular, artificial agents will need a principled approach to managing conflicting norms, which are common in human social interactions. Existing logic-based approaches suffer from normative explosion and are typically designed for deterministic environments; reward-based approaches lack principled ways of determining which normative alternatives exist in a given environment. We propose a hybrid approach, using Linear Temporal Logic (LTL) representations in Markov Decision Processes (MDPs), that manages norm conflicts in a systematic manner while accommodating domain stochasticity. We provide a proof-of-concept implementation in a simulated vacuum cleaning domain.
There have been several instances of temporal logics being employed to represent moral and social norms. For example, @cite_19 @cite_24 employ a logical representation of norms using Normative Temporal Logic, a generalization of Computation Tree Logic (CTL). This characterization allows the description of complex temporal norms. @cite_27 employ LTL with past-time modalities, which they use to construct guards (functions that restrict agent actions given an event history); they are concerned primarily with enforcement, and thus do not address norm conflicts. These approaches are designed for deterministic environment models, and are not well suited for stochastic domains.
@cite_24 @cite_19 @cite_27
2167352300 1986424898 2109187338 2049399166
Three temporal logics are introduced which induce on labeled transition systems the same identifications as branching bisimulation. The first is an extension of Hennessy-Milner logic with a kind of unit operator. The second is another extension of Hennessy-Milner logic which exploits the power of backward modalities. The third is CTL* with the next-time operator interpreted over all paths, not just over maximal ones. A relevant side effect of the last characterization is that it sets a bridge between the state- and event-based approaches to the semantics of concurrent systems. > This paper presents the linear temporal logic of rewriting (LTLR) model checker under localized fairness assumptions for the Maude system. The linear temporal logic of rewriting extends linear temporal logic (LTL) with spatial action patterns that describe patterns of rewriting events. Since LTLR generalizes and extends various state-based and event-based logics, mixed properties involving both state propositions and actions, such as fairness properties, can be naturally expressed in LTLR. However, often the needed fairness assumptions cannot even be expressed as propositional temporal logic formulas because they are parametric, that is, they correspond to universally quantified temporal logic formulas. Such universal quantification is succinctly captured by the notion of localized fairness; for example, fairness is localized to the object name parameter in object fairness conditions. We summarize the foundations, and present the language design and implementation of the Maude Fair LTLR model checker, developed at the C++ level within the Maude system by extending the existing Maude LTL model checker. Our tool provides not only an efficient LTLR model checking algorithm under parameterized fairness assumptions but also suitable specification languages as part of its user interface. The expressiveness and effectiveness of the Maude Fair LTLR model checker are illustrated by five case studies. This is the first tool we are aware of that can model check temporal logic properties under parameterized fairness assumptions. We develop the LTLR model checker under localized fairness assumptions.The linear temporal logic of rewriting (LTLR) extends LTL with action patterns.Localized fairness specifies parameterized fairness over generic system entities.We present the foundations, the language design, and the implementation of our tool.We illustrate the expressiveness and effectiveness of our tool with case studies. Temporal logic is two-valued: formulas are interpreted as either true or false. When applied to the analysis of stochastic systems, or systems with imprecise formal models, temporal logic is therefore fragile: even small changes in the model can lead to opposite truth values for a specification. We present a generalization of the branching-time logic CTL which achieves robustness with respect to model perturbations by giving a quantitative interpretation to predicates and logical operators, and by discounting the importance of events according to how late they occur. In every state, the value of a formula is a real number in the interval [0,1], where 1 corresponds to truth and 0 to falsehood. The boolean operators and and or are replaced by min and max, the path quantifiers ∃ and ¬ determine sup and inf over all paths from a given state, and the temporal operators ♦ and □ specify sup and inf over a given path; a new operator averages all values along a path. Furthermore, all path operators are discounted by a parameter that can be chosen to give more weight to states that are closer to the beginning of the path.We interpret the resulting logic DCTL over transition systems, Markov chains, and Markov decision processes. We present two semantics for DCTL: a path semantics, inspired by the standard interpretation of state and path formulas in CTL, and a fixpoint semantics, inspired by the µ-calculus evaluation of CTL formulas. We show that, while these semantics coincide for CTL, they differ for DCTL, and we provide model-checking algorithms for both semantics. In this paper, we consider the robust interpretation of Metric Temporal Logic (MTL) formulas over signals that take values in metric spaces. For such signals, which are generated by systems whose states are equipped with non-trivial metrics, for example continuous or hybrid, robustness is not only natural, but also a critical measure of system performance. Thus, we propose multi-valued semantics for MTL formulas, which capture not only the usual Boolean satisfiability of the formula, but also topological information regarding the distance, @e, from unsatisfiability. We prove that any other signal that remains @e-close to the initial one also satisfies the same MTL specification under the usual Boolean semantics. Finally, our framework is applied to the problem of testing formulas of two fragments of MTL, namely Metric Interval Temporal Logic (MITL) and closed Metric Temporal Logic (clMTL), over continuous-time signals using only discrete-time analysis. The motivating idea behind our approach is that if the continuous-time signal fulfills certain conditions and the discrete-time signal robustly satisfies the temporal logic specification, then the corresponding continuous-time signal should also satisfy the same temporal logic specification.