Visualizing transition diagrams of action language programs

Date

2002-10-30

Author

Koc, O
Alpaslan, Ferda Nur
Cicekli, NK

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

115
views

0
downloads

The subject of action languages is one of the prominent research topics in current Artificial Intelligence (AI) research. One of the problems in teaching and learning action languages as well as writing causal theory expressions is the difficulty of visualizing transition diagrams in mind. A tool, called TDV, which extends CCALC [GL98b] and uses GraphViz[KN91] software, is developed to visualize transition diagrams of C programs.

Subject Keywords

Action languages, Causal theories, Transition diagrams, Visualization

URI

https://hdl.handle.net/11511/53098

Conference Name

17th International Symposium on Computer and Information Sciences

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Generation and modification of 3D models with deep neural networks Öngün, Cihan; Temizel, Alptekin; Department of Information Systems (2021-9) Artificial intelligence (AI) and particularly deep neural networks (DNN) have become very hot topics in the recent years and they have been shown to be successful in problems such as detection, recognition and segmentation. More recently DNNs have started to be popular in data generation problems by the invention of Generative Adversarial Networks (GAN). Using GANs, various types of data such as audio, image or 3D models could be generated. In this thesis, we aim to propose a system that creates artificial...
Adapted Infinite Kernel Learning by Multi-Local Algorithm Akyuz, Sureyya Ozogur; Ustunkar, Gurkan; Weber, Gerhard Wilhelm (2016-05-01) The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for supp...
Context-aware markov decision processes Ekmekçi, Ömer; Polat, Faruk; Department of Computer Engineering (2014) In the 1990s, when artificial intelligence (AI) research become an important area again, researchers quitted trying to solve the ultimate problem, generating autonomous agents with "human-level intelligence". Currently, significantly important part of the research is highly focused on developing autonomous agents particularly dedicated to solve problems only in a chosen domain. Even though models and algorithms provided in reinforcement learning (RL), such as Markov Decision Processes (MDP), are successful ...
Estimating and reshaping human intention via human robot interaction DURDU, AKİF; Erkmen, İsmet; Erkmen, Aydan Müşerref (2016-01-01) Human robot interaction (HRI) is studied in two important research areas, intention estimation and intention reshaping. Although there are many studies in the literature that define human intention, new research examines the reshaping of human intentions by using robots in HRI. In this paper, 2 different robot movements are tested in a real environment in order to reshape current human intention. The hidden Markov model (HMM) is used to estimate human intention in our intelligent robotic system. The algorit...
A mathematical contribution of statistical learning and continuous optimization using infinite and semi-infinite programming to computational statistics Özöğür-Akyüz, Süreyya; Weber, Gerhard Wilhelm; Department of Scientific Computing (2009) A subfield of artificial intelligence, machine learning (ML), is concerned with the development of algorithms that allow computers to “learn”. ML is the process of training a system with large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). The most common machine learning schemes are supervised, semi-supervised, unsupervised and reinforcement learning. These schemes apply to natural language processing, search engines, medical diagnosis,...

Citation Formats

O. Koc, F. N. Alpaslan, and N. Cicekli, “Visualizing transition diagrams of action language programs,” presented at the 17th International Symposium on Computer and Information Sciences, UNIV CENT FLORIDA, ORLANDO, FL, 2002, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53098.