Toward Programming Languages for Reasoning: Humans, Symbolic Systems, and AI Agents

• URL: http://arxiv.org/abs/2407.06356v1
• Date: Mon, 8 Jul 2024 19:50:42 GMT
• Title: Toward Programming Languages for Reasoning: Humans, Symbolic Systems, and AI Agents
• Authors: Mark Marron,
• Abstract summary: Integration, composition, mechanization, and AI assisted development are the driving themes in the future of software development. This paper proposes a novel approach to this challenge -- instead of new language features or logical constructs, we propose radical simplification in the form of the Bosque platform and language.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integration, composition, mechanization, and AI assisted development are the driving themes in the future of software development. At their core these concepts are rooted in the increasingly important role of computing in our world, the desire to deliver functionality faster, with higher quality, and to empower more people to benefit from programmatic automation. These themes, and how they impact the human developers driving them, are the foundations for the next generation of programming languages. At first glance the needs of mechanization tools, AI agents, and human developers along with the various goals around development velocity, software quality, and software democratization are a broad and seemingly diverse set of needs. However, at their core is a single challenge that, once resolved, enables us to make radical progress in all of these areas. Our hypothesis is that, fundamentally, software development is a problem of reasoning about code and semantics. This is true for human developers implementing a feature, symbolic tools building models of application behavior, and even for language based AI agents as they perform tasks. While the particular aspects of reasoning that each agent struggles with varies to some degree, they share many common themes and, surprisingly, most mainstream languages extensively employ (anti)features that make this task harder or infeasible! This paper proposes a novel approach to this challenge -- instead of new language features or logical constructs, that add more complexity to what is already a problem of complexity, we propose radical simplification in the form of the Bosque platform and language.

Related papers

• Building Living Software Systems with Generative & Agentic AI [2.2481284426718533]
  Current software systems are static and inflexible, leading to challenges in translating human goals into computational actions. "Living software systems" powered by generative AI offer a solution to this fundamental problem in computing. Generative AI, particularly large language models, can serve as a universal translator between human intent and computer operations.
  arXiv  Detail & Related papers  (2024-08-03T12:35:30Z)
• OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
  We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer. We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T17:50:43Z)
• Symbolic Learning Enables Self-Evolving Agents [55.625275970720374]
  We introduce agent symbolic learning, a systematic framework that enables language agents to optimize themselves on their own. Agent symbolic learning is designed to optimize the symbolic network within language agents by mimicking two fundamental algorithms in connectionist learning. We conduct proof-of-concept experiments on both standard benchmarks and complex real-world tasks.
  arXiv  Detail & Related papers  (2024-06-26T17:59:18Z)
• Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers [30.996760992473064]
  We propose a paradigm shift towards goal-driven AI-powered pair programmers that collaborate with human developers. We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning.
  arXiv  Detail & Related papers  (2024-04-16T02:10:20Z)
• Cognition is All You Need -- The Next Layer of AI Above Large Language Models [0.0]
  We present Cognitive AI, a framework for neurosymbolic cognition outside of large language models. We propose that Cognitive AI is a necessary precursor for the evolution of the forms of AI, such as AGI, and specifically claim that AGI cannot be achieved by probabilistic approaches on their own. We conclude with a discussion of the implications for large language models, adoption cycles in AI, and commercial Cognitive AI development.
  arXiv  Detail & Related papers  (2024-03-04T16:11:57Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic Systems [67.01132165581667]
  We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components. We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
  arXiv  Detail & Related papers  (2023-11-13T21:20:17Z)
• PwR: Exploring the Role of Representations in Conversational Programming [17.838776812138626]
  We introduce Programming with Representations (PwR), an approach that uses representations to convey the system's understanding back to the user in natural language. We find that representations significantly improve understandability, and instilled a sense of agency among our participants.
  arXiv  Detail & Related papers  (2023-09-18T05:38:23Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• ChatDev: Communicative Agents for Software Development [84.90400377131962]
  ChatDev is a chat-powered software development framework in which specialized agents are guided in what to communicate. These agents actively contribute to the design, coding, and testing phases through unified language-based communication.
  arXiv  Detail & Related papers  (2023-07-16T02:11:34Z)
• Thinking Fast and Slow in AI: the Role of Metacognition [35.114607887343105]
  State-of-the-art AI still lacks many capabilities that would naturally be included in a notion of (human) intelligence. We argue that a better study of the mechanisms that allow humans to have these capabilities can help us understand how to imbue AI systems with these competencies.
  arXiv  Detail & Related papers  (2021-10-05T06:05:38Z)

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.