Preference at First Sight
We consider decision-making and game scenarios in which an agent is limited by his/her computational ability to foresee all the available moves towards the future - that is, we study scenarios with short sight. We focus on how short sight affects the logical properties of decision making in multi-agent settings. We start with single-agent sequential decision making (SSDM) processes, modeling them by a new structure of "preference-sight trees". Using this model, we first explore the relation between a new natural solution concept of Sight-Compatible Backward Induction (SCBI) and the histories produced by classical Backward Induction (BI). In particular, we find necessary and sufficient conditions for the two analyses to be equivalent. Next, we study whether larger sight always contributes to better outcomes. Then we develop a simple logical special-purpose language to formally express some key properties of our preference-sight models. Lastly, we show how short-sight SSDM scenarios call for substantial enrichments of existing fixed-point logics that have been developed for the classical BI solution concept. We also discuss changes in earlier modal logics expressing "surface reasoning" about best actions in the presence of short sight. Our analysis may point the way to logical and computational analysis of more realistic game models.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingSequential Decision MakingSimilar Papers 제목 키워드 기반
Who Cares More? Allocation with Diverse Preference Intensities
Goods and services -- public housing, medical appointments, schools -- are often allocated to individuals who rank them similarly but differ in their preference intensities. We characterize optimal allocation rules when …
Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing
The integration of preference alignment with diffusion models (DMs) has emerged as a transformative approach to enhance image generation and editing capabilities. Although integrating diffusion models with preference ali…
Autonomous DrivingImage GenerationNearly Optimal Active Preference Learning and Its Application to LLM Alignment
Aligning large language models (LLMs) depends on high-quality datasets of human preference labels, which are costly to collect. Although active learning has been studied to improve sample efficiency relative to passive c…
Active LearningAdvancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees
Tool-augmented large language models (LLMs) leverage tools, often in the form of APIs, to enhance their reasoning capabilities on complex tasks, thus taking on the role of intelligent agents interacting with the real wor…
Refining Alignment Framework for Diffusion Models with Intermediate-Step Preference Ranking
Direct preference optimization (DPO) has shown success in aligning diffusion models with human preference. Previous approaches typically assume a consistent preference label between final generations and noisy samples at…