Building a Generic Feedback System for Rule-Based Problems

Naus, Nico; Jeuring, Johan

doi:10.1007/978-3-030-14805-8_10

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Naus and Jeuring [19] have looked at building a generic feedback system for rule-based problems. A workflow system typically is rule based, as outlined in their work.…”

Section: Discussionmentioning

confidence: 99%

TopHat

Steenvoorden

Naus

Klinik

2019

Proceedings of the 21st International Symposium on Principles and Practice of Declarative Programming

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Software that models how people work is omnipresent in today's society. Current languages and frameworks often focus on usability by non-programmers, sacrificing flexibility and high level abstraction. Task-oriented programming (TOP) is a programming paradigm that aims to provide the desired level of abstraction while still being expressive enough to describe real world collaboration. It prescribes a declarative programming style to specify multi-user workflows. Workflows can be higher-order. They communicate through typed values on a local and global level. Such specifications can be turned into interactive applications for different platforms, supporting collaboration during execution. TOP has been around for more than a decade, in the forms of iTasks and mTasks, which are tailored for real-world usability. So far, it has not been given a formalisation which is suitable for formal reasoning.In this paper we give a description of the TOP paradigm and then decompose its rich features into elementary language elements, which makes them suitable for formal treatment. We use the simply typed lambda-calculus, extended with pairs and references, as a base language. On top of this language, we develop TopHat, a language for modular interactive workflows. We describe TopHat by means of a layered semantics. These layers consist of multiple big-step evaluations on expressions, and two labelled transition systems, handling user inputs.With TopHat we prepare a way to formally reason about TOP languages and programs. This approach allows for comparison with other work in the field. We have implemented the semantic rules of TopHat in Haskell, and the task layer on top of the iTasks framework. This shows that our approach is feasible, and lets us demonstrate the concepts by means of illustrative case studies. TOP has been applied in projects with the Dutch coast guard, tax office, and navy. Our work matters because formal program verification is important for mission-critical software, especially for systems with concurrency.

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“…Naus and Jeuring [19] have looked at building a generic feedback system for rule-based problems. A workflow system typically is rule based, as outlined in their work.…”

Section: Discussionmentioning

confidence: 99%

TopHat

Steenvoorden

Naus

Klinik

2019

Proceedings of the 21st International Symposium on Principles and Practice of Declarative Programming

Self Cite

View full text Add to dashboard Cite

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“…In previous work, we have attempted to provide end users with next step hints by viewing workflows as rule based problems [15]. By abstracting over workflows, reasoning about them becomes simpler.…”

Section: Related Workmentioning

confidence: 99%

“…To overcome these difficulties, we propose to integrate a next step hint system into workflow software. By combining previous research on symbolic execution for Task-Oriented Programming [16] and end-user feedback systems for rule based problems [15], we develop a next step hint end-user feedback system for the Task-Oriented Programming language TopHat ( TOP) [20]. Our solution, which we call Assistive TOP, generates next step hints from existing code, and does not require extra work by the programmer.…”

Section: Introductionmentioning

confidence: 99%

Generating Next Step Hints for Task Oriented Programs Using Symbolic Execution

Naus

Steenvoorden

2020

Lecture Notes in Computer Science

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Software that models business workflows is omnipresent in today's society. These systems coordinate collaboration in hospitals, companies, and military institutions. Unfortunately, workflow systems may obfuscate the influence of current user actions on the desired end result. In order to make the right decision, users need to oversee the full process and all information available, both of which are usually buried in the system. We have developed a way to automatically generate next step hints for task oriented programs. Task oriented programming provides programmers with an abstraction over workflow software, while still being expressive enough to describe real world collaboration. By leveraging symbolic execution, we can calculate these hints without modification of the original program. To our knowledge, this is the first time that symbolic execution is used to automatically generate next step hints for end users. We prove the generated hints to be sound and complete, and also demonstrate that the symbolic execution semantics we employ is correct for sequential input. In addition, we have developed a Haskell implementation of our automatic next step hint generation system. By providing next step hints, the chance of human error is reduced, while still allowing end users to intervene if required. The overall performance is raised, since the quality of decisions will improve.

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“…Designing a real-time controller for robots with high-speed motion is a challenging problem. Conventional approaches of designing a rule-based feedback controller [1]- [3] have shortcomings in covering all the exception cases. Recent approaches include a method that can autonomously cover all the exception cases, such as training the controller via deep reinforcement learning (RL) methods [4]- [8].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Imitation Learning with Mixed Qualities of Demonstrations for Autonomous Driving

Lee¹,

Oh²,

Shin³

et al. 2021

Preprint

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In this paper, we consider the problem of autonomous driving using imitation learning in a semi-supervised manner. In particular, both labeled and unlabeled demonstrations are leveraged during training by estimating the quality of each unlabeled demonstration. If the provided demonstrations are corrupted and have a low signal-to-noise ratio, the performance of the imitation learning agent can be degraded significantly. To mitigate this problem, we propose a method called semi-supervised imitation learning (SSIL). SSIL first learns how to discriminate and evaluate each state-action pair's reliability in unlabeled demonstrations by assigning higher reliability values to demonstrations similar to labeled expert demonstrations. This reliability value is called leverage. After this discrimination process, both labeled and unlabeled demonstrations with estimated leverage values are utilized while training the policy in a semi-supervised manner. The experimental results demonstrate the validity of the proposed algorithm using unlabeled trajectories with mixed qualities. Moreover, the hardware experiments using an RC car are conducted to show that the proposed method can be applied to real-world applications.

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Building a Generic Feedback System for Rule-Based Problems

Cited by 5 publications

References 14 publications

TopHat

TopHat

Generating Next Step Hints for Task Oriented Programs Using Symbolic Execution

Semi-Supervised Imitation Learning with Mixed Qualities of Demonstrations for Autonomous Driving

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