Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAn Integrated Development Environment for Planning Domain Modeling
422
0
0.0
(
0
)
Added by
Yuncong Li
Publication date
2018
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
In order to make the task, description of planning domains and problems, more comprehensive for non-experts in planning, the visual representation has been used in planning domain modeling in recent years. However, current knowledge engineering tools with visual modeling, like itSIMPLE (Vaquero et al. 2012) and VIZ (Vodrav{z}ka and Chrpa 2010), are less efficient than the traditional method of hand-coding by a PDDL expert using a text editor, and rarely involved in finetuning planning domains depending on the plan validation. Aim at this, we present an integrated development environment KAVI for planning domain modeling inspired by itSIMPLE and VIZ. KAVI using an abstract domain knowledge base to improve the efficiency of planning domain visual modeling. By integrating planners and a plan validator, KAVI proposes a method to fine-tune planning domains based on the plan validation.
rate research
Read More
In recent years, program verifiers and interactive theorem provers have become more powerful and more suitable for verifying large programs or proofs. This has demonstrated the need for improving the user experience of these tools to increase productivity and to make them more accessible to non-experts. This paper presents an integrated development environment for Dafny-a programming language, verifier, and proof assistant-that addresses issues present in most state-of-the-art verifiers: low responsiveness and lack of support for understanding non-obvious verification failures. The paper demonstrates several new features that move the state-of-the-art closer towards a verification environment that can provide verification feedback as the user types and can present more helpful information about the program or failed verifications in a demand-driven and unobtrusive way.
This volume contains the proceedings of F-IDE 2019, the fifth international workshop on Formal Integrated Development Environment, which was held on October 7, 2019 in Porto, Portugal, as part of FM19, the 3rd World Congress on Formal Methods. High levels of safety, security and privacy standards require the use of formal methods to specify and develop compliant software (sub)systems. Any standard comes with an assessment process, which requires a complete documentation of the application in order to ease the justification of design choices and the review of code and proofs. Thus tools are needed for handling specifications, program constructs and verification artifacts. The aim of the F-IDE workshop is to provide a forum for presenting and discussing research efforts as well as experience returns on design, development and usage of formal IDE aiming at making formal methods easier for both specialists and non-specialists.
Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents abilities to evolve or learn capable and robust strategies.
The purpose of this paper is to present some functionalities of the HyperPro System. HyperPro is a hypertext tool which allows to develop Constraint Logic Programming (CLP) together with their documentation. The text editing part is not new and is based on the free software Thot. A HyperPro program is a Thot document written in a report style. The tool is designed for CLP but it can be adapted to other programming paradigms as well. Thot offers navigation and editing facilities and synchronized static document views. HyperPro has new functionalities such as document exportations, dynamic views (projections), indexes and version management. Projection is a mechanism for extracting and exporting relevant pieces of code program or of document according to specific criteria. Indexes are useful to find the references and occurrences of a relation in a document, i.e., where its predicate definition is found and where a relation is used in other programs or docume
The ability to quickly solve a wide range of real-world tasks requires a commonsense understanding of the world. Yet, how to best extract such knowledge from natural language corpora and integrate it with reinforcement learning (RL) agents remains an open challenge. This is partly due to the lack of lightweight simulation environments that sufficiently reflect the semantics of the real world and provide knowledge sources grounded with respect to observations in an RL environment. To better enable research on agents making use of commonsense knowledge, we propose WordCraft, an RL environment based on Little Alchemy 2. This lightweight environment is fast to run and built upon entities and relations inspired by real-world semantics. We evaluate several representation learning methods on this new benchmark and propose a new method for integrating knowledge graphs with an RL agent.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
