Design Support for Multitape Turing Machines

• URL: http://arxiv.org/abs/2508.03638v1
• Date: Tue, 05 Aug 2025 16:53:19 GMT
• Title: Design Support for Multitape Turing Machines
• Authors: Marco T. Morazán, Oliwia Kempinski, Andrés M. Garced,
• Abstract summary: Many Formal Languages and Automata Theory courses introduce students to Turing machine extensions.<n>One of the most widely-used extensions endows Turing machines with multiple tapes.<n>Students find multitape Turing machines no less challenging.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many Formal Languages and Automata Theory courses introduce students to Turing machine extensions. One of the most widely-used extensions endows Turing machines with multiple tapes. Although multitape Turing machines are an abstraction to simplify Turing machine design, students find them no less challenging. To aid students in understanding these machines, the FSM programming language provides support for their definition and execution. This, however, has proven insufficient for many students to understand the operational semantics of such machines and to understand why such machines accept or reject a word. To address this problem, three visualization tools have been developed. The first is a dynamic visualization tool that simulates machine execution. The second is a static visualization tool that automatically renders a graphic for a multitape Turing machine's transition diagram. The third is a static visualization tool that automatically renders computation graphs for multitape Turing machines. This article presents these tools and illustrates how they are used to help students design and implement multitape Turing machines. In addition, empirical data is presented that suggests these tools are well-received and found useful by students.

Related papers

• Visual Execution and Validation of Finite-State Machines and Pushdown Automata [0.0]
  In Formal Languages and Automata Theory courses, students find understanding nondeterministic finite-state and pushdown automata difficult.<n>We present two novel dynamic visualization tools for FSM to support the design of such machines.<n>These tools visualize all computations that may be performed, respectively, by a nondeterministic finite-state machine or by a pushdown automata in a stepwise manner.
  arXiv  Detail & Related papers  (2025-08-05T16:54:01Z)
• Finite State Machine with Input and Process Render [0.22940141855172028]
  In this poster, we created an automatic visualization tool for Finite State Machines (FSMs) that generates videos of FSM simulation. Educators can input any formal definition of an FSM and an input string, and FSMIPR generates an accompanying video of its simulation.
  arXiv  Detail & Related papers  (2024-09-25T16:14:15Z)
• FSM Builder: A Tool for Writing Autograded Finite Automata Questions [0.5018156030818883]
  We introduce the FSM Builder, a new pedagogical tool enabling students to practice constructing DFAs and NFAs with a graphical editor. The algorithms used for generating these are heavily inspired by previous works. We discuss the implementation of the tool, how it stands out from previous tools, and takeaways from experiences of using the tool in multiple large courses.
  arXiv  Detail & Related papers  (2024-05-02T20:25:25Z)
• ControlLLM: Augment Language Models with Tools by Searching on Graphs [97.62758830255002]
  We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving real-world tasks. Our framework comprises three key components: (1) a textittask decomposer that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a textitThoughts-on-Graph (ToG) paradigm that searches the optimal solution path on a pre-built tool graph; and (3) an textitexecution engine with a rich toolbox that interprets the solution path and runs the
  arXiv  Detail & Related papers  (2023-10-26T21:57:21Z)
• Can I say, now machines can think? [0.0]
  We analyzed and explored the capabilities of artificial intelligence-enabled machines. Turing Test is a critical aspect of evaluating machines' ability. There are other aspects of intelligence too, and AI machines exhibit most of these aspects.
  arXiv  Detail & Related papers  (2023-07-11T11:44:09Z)
• Token Turing Machines [53.22971546637947]
  Token Turing Machines (TTM) is a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is inspired by the seminal Neural Turing Machine, and has an external memory consisting of a set of tokens which summarise the previous history.
  arXiv  Detail & Related papers  (2022-11-16T18:59:18Z)
• What Images are More Memorable to Machines? [87.14558566342322]
  Similar to humans, machines also tend to memorize certain kinds of images, whereas the types of images that machines and humans are different. This work proposes the concept of machine memorability and opens a new research direction at the interface between machine memory and visual data.
  arXiv  Detail & Related papers  (2022-11-14T18:48:08Z)
• Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions [58.220137936626315]
  This paper extensively discusses automated graph machine learning approaches. We introduce AutoGL, our dedicated and the world's first open-source library for automated graph machine learning. Also, we describe a tailored benchmark that supports unified, reproducible, and efficient evaluations.
  arXiv  Detail & Related papers  (2022-01-04T18:31:31Z)
• Automated Machine Learning on Graphs: A Survey [81.21692888288658]
  This paper is the first systematic and comprehensive review of automated machine learning on graphs. We focus on hyper- parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. In the end, we share our insights on future research directions for automated graph machine learning.
  arXiv  Detail & Related papers  (2021-03-01T04:20:33Z)
• Reservoir memory machines [79.79659145328856]
  We propose reservoir memory machines, which are able to solve some of the benchmark tests for Neural Turing Machines. Our model can also be seen as an extension of echo state networks with an external memory, enabling arbitrarily long storage without interference.
  arXiv  Detail & Related papers  (2020-02-12T01:45:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.