Multi-Objective Search: Algorithms, Applications, and Emerging Directions

• URL: http://arxiv.org/abs/2510.25504v1
• Date: Wed, 29 Oct 2025 13:30:01 GMT
• Title: Multi-Objective Search: Algorithms, Applications, and Emerging Directions
• Authors: Oren Salzman, Carlos Hernández Ulloa, Ariel Felner, Sven Koenig,
• Abstract summary: Multi-objective search (MOS) has emerged as a unifying framework for planning and decision-making problems.<n>Recent years have seen renewed interest in the topic across AI applications such as robotics, transportation, and operations research.
• Score: 17.912861906717563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-objective search (MOS) has emerged as a unifying framework for planning and decision-making problems where multiple, often conflicting, criteria must be balanced. While the problem has been studied for decades, recent years have seen renewed interest in the topic across AI applications such as robotics, transportation, and operations research, reflecting the reality that real-world systems rarely optimize a single measure. This paper surveys developments in MOS while highlighting cross-disciplinary opportunities, and outlines open challenges that define the emerging frontier of MOS

Related papers

• Deep Research: A Systematic Survey [118.82795024422722]
  Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
  arXiv  Detail & Related papers  (2025-11-24T15:28:28Z)
• Multi-Objective Bilevel Learning [12.198330173886587]
  We investigate the theoretical and algorithmic foundation of multi-objective bilevel learning (MOBL)<n>Our goal is to develop efficient MOBL optimization algorithms.<n>We propose a unifying algorithmic framework called weighted-Chebyshev multi-hyper-gradient-descent (WC-MHGD)
  arXiv  Detail & Related papers  (2025-11-11T04:42:14Z)
• Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
  We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
  arXiv  Detail & Related papers  (2025-10-07T17:49:24Z)
• Generalizing Multi-Objective Search via Objective-Aggregation Functions [7.453186116194618]
  Multi-objective search (MOS) has become essential in robotics, as real-world robotic systems need to balance multiple, often conflicting objectives.<n>Recent works explore complex interactions between objectives, leading to problem formulations that do not allow the usage of out-of-the-box state-of-the-art MOS algorithms.<n>We suggest a generalized problem formulation that optimize solution objectives via aggregation functions of hidden (search) objectives.
  arXiv  Detail & Related papers  (2025-09-26T09:06:03Z)
• Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey [124.23247710880008]
  multimodal CoT (MCoT) reasoning has recently garnered significant research attention.<n>Existing MCoT studies design various methodologies to address the challenges of image, video, speech, audio, 3D, and structured data.<n>We present the first systematic survey of MCoT reasoning, elucidating the relevant foundational concepts and definitions.
  arXiv  Detail & Related papers  (2025-03-16T18:39:13Z)
• On Generalization Across Environments In Multi-Objective Reinforcement Learning [6.686583184622338]
  We formalize the concept of generalization in Multi-Objective Reinforcement Learning (MORL) and how it can be evaluated.<n>We contribute a novel benchmark featuring diverse multi-objective domains with parameterized environment configurations.<n>Our baseline evaluations of state-of-the-art MORL algorithms on this benchmark reveals limited generalization capabilities, suggesting significant room for improvement.
  arXiv  Detail & Related papers  (2025-03-02T08:50:14Z)
• A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond [84.95530356322621]
  This survey presents a systematic review of the advancements in code intelligence.<n>It covers over 50 representative models and their variants, more than 20 categories of tasks, and an extensive coverage of over 680 related works.<n>Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence.
  arXiv  Detail & Related papers  (2024-03-21T08:54:56Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• Multi-agent Reinforcement Learning: A Comprehensive Survey [10.186029242664931]
  Multi-agent systems (MAS) are widely prevalent and crucially important in numerous real-world applications. Despite their ubiquity, the development of intelligent decision-making agents in MAS poses several open challenges to their effective implementation. This survey examines these challenges, placing an emphasis on studying seminal concepts from game theory (GT) and machine learning (ML)
  arXiv  Detail & Related papers  (2023-12-15T23:16:54Z)
• Task Allocation using a Team of Robots [29.024300177453824]
  We present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate. We describe how the problem can vary depending on the feasibility constraints, objective functions, and the level of dynamically changing information.
  arXiv  Detail & Related papers  (2022-07-20T04:49:11Z)
• Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata [87.77379512999818]
  We provide an overview of recent progress in the use of continuous-time perspectives in the analysis and design of methods targeting the broad VI problem class. Our presentation draws parallels between single-objective problems and multi-objective problems, highlighting the challenges of the latter. We also formulate various desiderata for algorithms that apply to general VIs and we argue that achieving these desiderata may profit from an understanding of the associated continuous-time dynamics.
  arXiv  Detail & Related papers  (2022-07-14T17:58:02Z)

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.