Characterizing Novelty in the Military Domain

• URL: http://arxiv.org/abs/2302.12314v1
• Date: Thu, 23 Feb 2023 20:21:24 GMT
• Title: Characterizing Novelty in the Military Domain
• Authors: Theresa Chadwick, James Chao, Christianne Izumigawa, George Galdorisi, Hector Ortiz-Pena, Elias Loup, Nicholas Soultanian, Mitch Manzanares, Adrian Mai, Richmond Yen, and Douglas S. Lange
• Abstract summary: In operation, a rich environment is likely to present challenges not seen in training sets or accounted for in engineered models. A program at the Defense Advanced Research Project Agency (DARPA) seeks to develop agents that are robust to novelty. This capability will be required, before AI has the role envisioned within mission critical environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A critical factor in utilizing agents with Artificial Intelligence (AI) is their robustness to novelty. AI agents include models that are either engineered or trained. Engineered models include knowledge of those aspects of the environment that are known and considered important by the engineers. Learned models form embeddings of aspects of the environment based on connections made through the training data. In operation, however, a rich environment is likely to present challenges not seen in training sets or accounted for in engineered models. Worse still, adversarial environments are subject to change by opponents. A program at the Defense Advanced Research Project Agency (DARPA) seeks to develop the science necessary to develop and evaluate agents that are robust to novelty. This capability will be required, before AI has the role envisioned within mission critical environments. As part of the Science of AI and Learning for Open-world Novelty (SAIL-ON), we are mapping possible military domain novelty types to a domain-independent ontology developed as part of a theory of novelty. Characterizing the possible space of novelty mathematically and ontologically will allow us to experiment with agent designs that are coming from the DARPA SAIL-ON program in relevant military environments. Utilizing the same techniques as being used in laboratory experiments, we will be able to measure agent ability to detect, characterize, and accommodate novelty.

Related papers

• Probing for Consciousness in Machines [3.196204482566275]
  This study explores the potential for artificial agents to develop core consciousness. The emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. Our results demonstrate that the agent can form rudimentary world and self models, suggesting a pathway toward developing machine consciousness.
  arXiv  Detail & Related papers  (2024-11-25T10:27:07Z)
• EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment [38.14321677323052]
  Embodied artificial intelligence emphasizes the role of an agent's body in generating human-like behaviors. In this paper, we construct a benchmark platform for embodied intelligence evaluation in real-world city environments.
  arXiv  Detail & Related papers  (2024-10-12T17:49:26Z)
• Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack [7.253801704452419]
  Recent advances in neuromorphic computing with Spiking Neural Networks (SNN) have demonstrated the potential to enable the embodied intelligence for robotics. This paper will discuss how we can enable embodied neuromorphic AI for robotic systems through our perspectives.
  arXiv  Detail & Related papers  (2024-04-04T09:52:22Z)
• 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)
• On the Emergence of Symmetrical Reality [51.21203247240322]
  We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations. We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
  arXiv  Detail & Related papers  (2024-01-26T16:09:39Z)
• 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)
• 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)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• OPEn: An Open-ended Physics Environment for Learning Without a Task [132.6062618135179]
  We will study if models of the world learned in an open-ended physics environment, without any specific tasks, can be reused for downstream physics reasoning tasks. We build a benchmark Open-ended Physics ENvironment (OPEn) and also design several tasks to test learning representations in this environment explicitly. We find that an agent using unsupervised contrastive learning for representation learning, and impact-driven learning for exploration, achieved the best results.
  arXiv  Detail & Related papers  (2021-10-13T17:48:23Z)
• From SLAM to Situational Awareness: Challenges and Survey [0.0]
  The capability of a mobile robot to efficiently and safely perform complex missions is limited by its knowledge of the environment. Advanced reasoning, decision-making, and execution skills enable an intelligent agent to act autonomously in unknown environments. This paper investigates each aspect of Situational Awareness, surveying the state-of-the-art robotics algorithms that cover them.
  arXiv  Detail & Related papers  (2021-10-01T09:00:34Z)

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.