Preparing for Super-Reactivity: Early Fault-Detection in the Development of Exceedingly Complex Reactive Systems

• URL: http://arxiv.org/abs/2410.02627v2
• Date: Sat, 26 Oct 2024 08:35:21 GMT
• Title: Preparing for Super-Reactivity: Early Fault-Detection in the Development of Exceedingly Complex Reactive Systems
• Authors: David Harel, Assaf Marron,
• Abstract summary: We introduce the term Super-Reactive Systems to refer to reactive systems whose construction and behavior are complex, constantly changing and evolving. Finding hidden faults in such systems early in planning and development is critical for human safety, the environment, society and the economy. We propose an architecture for models and tools to overcome barriers and enable simulation, systematic analysis, and fault detection and handling.
• Score: 1.6298172960110866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the term Super-Reactive Systems to refer to reactive systems whose construction and behavior are complex, constantly changing and evolving, and heavily interwoven with other systems and the physical world. Finding hidden faults in such systems early in planning and development is critical for human safety, the environment, society and the economy. However, the complexity of the system and its interactions and the absence of adequate technical details pose a great obstacle. We propose an architecture for models and tools to overcome such barriers and enable simulation, systematic analysis, and fault detection and handling, early in the development of super-reactive systems. The approach is facilitated by the inference and abstraction capabilities and the power and knowledge afforded by large language models and associated AI tools. It is based on: (i) deferred, just-in-time interpretation of model elements that are stored in natural language form, and (ii) early capture of tacit interdependencies among seemingly orthogonal requirements.

Related papers

• A Survey on Context-Aware Multi-Agent Systems: Techniques, Challenges and Future Directions [1.1458366773578277]
  Research interest in autonomous agents is on the rise as an emerging topic. The challenge lies in enabling these agents to learn, reason, and navigate uncertainties in dynamic environments. Context awareness emerges as a pivotal element in fortifying multi-agent systems.
  arXiv  Detail & Related papers  (2024-02-03T00:27:22Z)
• A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systems [41.055298739292695]
  Emergence, a global property of complex adaptive systems, is prevalent in real-world dynamic systems, e.g., network-level traffic congestions. This paper proposes a hierarchical framework with CAS-temporal consistency to solve these two problems by learning the system representation and agent representations. Our method achieves more detection than traditional methods and deep learning methods on three datasets with well-known yet hard-to-detect emergent behaviors.
  arXiv  Detail & Related papers  (2024-01-18T08:55:05Z)
• Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
  "Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data. We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
  arXiv  Detail & Related papers  (2024-01-07T19:11:18Z)
• 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)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• Don't Treat the Symptom, Find the Cause! Efficient Artificial-Intelligence Methods for (Interactive) Debugging [0.0]
  In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication. In this thesis, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these issues.
  arXiv  Detail & Related papers  (2023-06-22T12:44:49Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Survey on Deep Fuzzy Systems in regression applications: a view on interpretability [1.2158275183241178]
  Regression problems have been more and more embraced by deep learning (DL) techniques. Accessing the interpretability of these models is an essential factor for addressing problems in sensitive areas. This paper aims to investigate the state-of-the-art on existing methodologies that combine DL and FLS.
  arXiv  Detail & Related papers  (2022-09-09T10:40:31Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• Machine learning dismantling and early-warning signals of disintegration in complex systems [0.0]
  We show that a machine trained to dismantle relatively small systems is able to identify higher-order topological patterns. Remarkably, the machine assesses the probability that next attacks will disintegrate the system.
  arXiv  Detail & Related papers  (2021-01-07T09:39:13Z)

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.