Scenario-based specification approaches offer system engineering advantages with their intuitiveness, executability, and amenability to formal verification and synthesis. However, many engineering tasks such as debugg...
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(纸本)9789897582837
Scenario-based specification approaches offer system engineering advantages with their intuitiveness, executability, and amenability to formal verification and synthesis. However, many engineering tasks such as debugging or maintenance are still far from trivial even when using such specifications. Specifically, it is hard to find out why a complex system behaves as it does, or how it would behave under certain conditions. Here, we present work in progress towards the (semi-)automatic analysis of event traces emanating from simulation runs and actual executions. These traces may be large, yet developers are often interested only in specific properties thereof, like is any specification property violated? are particular properties demonstrated? is there a smaller sub-sequence of events that violates or demonstrates the same properties? which trace properties are common to multiple traces and which are unique? etc. Our approach includes automatic techniques for discovering and distilling relevant properties of traces, analyzing properties of sets of traces, using (sets of) execution traces for understanding specified and actual system behavior and problems therein, planning system enhancement and repair, and more. Our work leverages and extends existing work on trace summarization, formal methods for model analysis, specification mining from execution traces, and others, in the context of scenario-based specifications. A key guiding perspective for this research is that interesting properties of a trace often can be associated with one or very few concise scenarios, depicting desired or forbidden behavior, which are already in the specification, or should be added to it.
Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive r...
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Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.
The least restrictive alternative concept is widely used in mental health law. This paper addresses how the concept has been applied to treatment decisions. The paper offers both a legal and a behavioral analysis to s...
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The least restrictive alternative concept is widely used in mental health law. This paper addresses how the concept has been applied to treatment decisions. The paper offers both a legal and a behavioral analysis to some problems that have emerged in recent years concerning the selection of behavioral procedures used to change client behavior. The paper also offers ways of improving the application of the concept, which involve developing a more behaviorally functional perspective toward restrictiveness.
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