Improving the model checking of strategies under partial observability and fairness constraints

Reasoning about strategies has been a concern for several years, and many extensions of Alternating-time Temporal Logic have been proposed. One extension, ATLKirF , allows the user to reason about the strategies of the agents of a system under partial observability and unconditional fairness constraints. However, the existing model-checking algorithm for ATLKirF is inefficient when the user is only interested in the satisfaction of a formula in a small subset of states, such as the set of initial states of the system. We propose to generate fewer strategies by only focusing on partial strategies reachable from this subset of states, reducing the time needed to perform the verification. We also describe several practical improvements to further reduce the verification time and present experiments showing the practical impact of the approach

Reasoning about strategies under partial observability and fairness constraints

A number of extensions exist for Alternating-time Temporal Logic; some of these mix strategies and partial observability but, to the best of our knowledge, no work provides a unified framework for strategies, partial observability and fairness constraints. In this paper we propose AT LK^F_po, a logic mixing strategies under partial observability and epistemic properties of agents in a system with fairness constraints on states, and we provide a model checking algorithm for i

Comparing approaches for model-checking strategies under imperfect information and fairness constraints

Starting from Alternating-time Temporal Logic, many logics for reasoning about strategies in a system of agents have been proposed. Some of them consider the strategies that agents can play when they have partial information about the state of the system. ATLKirF is such a logic to reason about uniform strategies under unconditional fairness constraints. While this kind of logics has been extensively studied, practical approaches for solving their model- checking problem appeared only recently. This paper considers three approaches for model checking strategies under partial observability of the agents, applied to ATLKirF . These three approaches have been implemented in PyNuSMV, a Python library based on the state-of- the-art model checker NuSMV. Thanks to the experimental results obtained with this library and thanks to the comparison of the relative performance of the approaches, this paper provides indications and guidelines for the use of these verification techniques, showing that different approaches are needed in different situations

Improving the model checking of strategies under partial observability and fairness constraints

Reasoning about strategies has been a concern for several years, and many extensions of Alternating-time Temporal Logic have been proposed. One extension, ATLKirF , allows the user to reason about the strategies of the agents of a system under partial observability and unconditional fairness constraints. However, the existing model-checking algorithm for ATLKirF is inefficient when the user is only interested in the satisfaction of a formula in a small subset of states, such as the set of initial states of the system. We propose to generate fewer strategies by only focusing on partial strategies reachable from this subset of states, reducing the time needed to perform the verification. We also describe several practical improvements to further reduce the verification time and present experiments showing the practical impact of the approach

Rich Counter-Examples for Temporal-Epistemic Logic Model Checking

Model checking verifies that a model of a system satisfies a given property, and otherwise produces a counter-example explaining the violation. The verified properties are formally expressed in temporal logics. Some temporal logics, such as CTL, are branching: they allow to express facts about the whole computation tree of the model, rather than on each single linear computation. This branching aspect is even more critical when dealing with multi-modal logics, i.e. logics expressing facts about systems with several transition relations. A prominent example is CTLK, a logic that reasons about temporal and epistemic properties of multi-agent systems. In general, model checkers produce linear counter-examples for failed properties, composed of a single computation path of the model. But some branching properties are only poorly and partially explained by a linear counter-example. This paper proposes richer counter-example structures called tree-like annotated counter-examples (TLACEs), for properties in Action-Restricted CTL (ARCTL), an extension of CTL quantifying paths restricted in terms of actions labeling transitions of the model. These counter-examples have a branching structure that supports more complete description of property violations. Elements of these counter-examples are annotated with parts of the property to give a better understanding of their structure. Visualization and browsing of these richer counter-examples become a critical issue, as the number of branches and states can grow exponentially for deeply-nested properties. This paper formally defines the structure of TLACEs, characterizes adequate counter-examples w.r.t. models and failed properties, and gives a generation algorithm for ARCTL properties. It also illustrates the approach with examples in CTLK, using a reduction of CTLK to ARCTL. The proposed approach has been implemented, first by extending the NuSMV model checker to generate and export branching counter-examples, secondly by providing an interactive graphical interface to visualize and browse them.Comment: In Proceedings IWIGP 2012, arXiv:1202.422

Optimizing adalimumab treatment in psoriasis with concomitant methotrexate (OPTIMAP): study protocol for a pragmatic, single-blinded, investigator-initiated randomized controlled trial

markdownabstract__Background:__ The introduction of anti-tumor necrosis factor medications has revolutionized the treatment of psoriasis with achievement of treatment goals (Psoriasis Area and Severity Index score 75, remission) that are not usually met with conventional systemics. Nevertheless, some patients continue to experience persistent disease activity or treatment failure over time. Strategies to optimize treatment outcomes include the use of concomitant methotrexate, which has demonstrated beneficial effects on pharmacokinetics and treatment efficacy in psoriasis and other inflammatory diseases. __Methods:__ This is an investigator-initiated, multicenter randomized controlled trial (RCT) designed to compare the combination treatment of adalimumab and methotrexate with adalimumab monotherapy in patients with psoriasis. The primary outcome is adalimumab drug survival at week 49. Other outcomes include improvement in disease severity and quality of life, tolerability, and safety. Moreover, anti-adalimumab antibodies and adalimumab serum concentrations will be measured and correlations between genotypes and clinical outcomes will be assessed. Patient recruitment started in March 2014. Up to now, 36 patients have been randomized. Many more patients have been (pre)screened. A total of 93 patients is desired to meet an adequate sample size. In our experience, the main limitation for recruitment is prior adalimumab therapy and intolerability or toxicity for methotrexate in the past. __Discussion:__ OPTIMAP is the first RCT to examine combination therapy with adalimumab and methotrexate in a psoriasis population. With data derived from this study we expect to provide valuable clinical data on long-term treatment outcomes. These data will be supported by assessment of the impact of concomitant methotrexate on adalimumab pharmacokinetics. Furthermore, the influence of several single nucleotide polymorphisms on adalimumab response will be analyzed in order to support the development of a more personalized approach for this targeted therapy. Trial registration:NTR4499. Registered on 7 April 2014

Core outcome sets in dermatology: report from the second meeting of the International Cochrane Skin Group Core Outcome Set Initiative

Results of clinical trials are the most important information source for generating external clinical evidence. The use of different outcomes across trials, which investigate similar interventions for similar patient groups, significantly limits the interpretation, comparability and clinical application of trial results. Core outcome sets (COSs) aim to overcome this limitation. A COS is an agreed standardized collection of outcomes that should be measured and reported in all clinical trials for a specific clinical condition. The Core Outcome Set Initiative within the Cochrane Skin Group (CSG-COUSIN) supports the development of core outcomes in dermatology. In the second CSG-COUSIN meeting held in 2017, 11 COS development groups working on skin diseases presented their current work. The presentations and discussions identified the following overarching methodological challenges for COS development in dermatology: it is not always easy to define the disease focus of a COS; the optimal method for outcome domain identification and level of detail needed to specify such domains is challenging to many; decision rules within Delphi surveys need to be improved; appropriate ways of patient involvement are not always clear. In addition, there appear to be outcome domains that may be relevant as potential core outcome domains for the majority of skin diseases. The close collaboration between methodologists in the Core Outcome Set Initiative and the international Cochrane Skin Group has major advantages for trialists, systematic reviewers and COS developers