Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies

核心概念

Human preferences in decision-making and probabilistic planning are efficiently modeled using partially-ordered temporal goals, leading to the synthesis of most preferred policies.

摘要

The content discusses preference-based planning in stochastic systems using Markov decision processes (MDPs) with user preferences over temporally extended goals expressed in Linear Temporal Logic on Finite Traces (LTLf). The paper introduces a novel computational model, the Preference Deterministic Finite Automaton (PDFA), to map preferences over temporal goals to policies for MDPs. The algorithm presented transforms partially ordered preferences over temporal goals into a computational model, enabling the computation of nondominated policies. The paper provides detailed insights into the technical approach, illustrating the proposed preference specification and solution methods with running examples. It also explores the implications of the algorithm and discusses potential future directions.
Structure:

Abstract
Introduction
Preference-Based Planning in Stochastic Systems
Modeling Temporal Goals
Ranking Policies
Construction of PDFA
Example and Analysis

統計資料

"Bob has a preference for how the robot should achieve the task of pollination."
"Tulips have a shorter life span, so Bob considers four outcomes."
"The robot might not achieve the most preferred outcome with probability one."

引述

"Preferences may need to admit a partial order because of inescapability and incommensurability."
"Preference-based planning enables the system to decide which goals to satisfy when not all of them can be achieved."

從以下內容提煉的關鍵洞見

Preference-Based Planning in Stochastic Environments

by Hazhar Rahma... 於 arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18212.pdf

深入探究

How can preference-based planning be applied in other fields beyond artificial intelligence

Preference-based planning can be applied in various fields beyond artificial intelligence, such as:

Robotics: Preference-based planning can be used to optimize robot actions based on user preferences, making human-robot interactions more intuitive and efficient.

Healthcare: In healthcare, preference-based planning can help personalize treatment plans based on patient preferences, improving patient satisfaction and outcomes.

Finance: Preference-based planning can assist in personalized financial planning, investment strategies, and risk management tailored to individual preferences and goals.

Supply Chain Management: By incorporating preferences into supply chain decisions, companies can optimize inventory management, distribution, and logistics based on customer preferences and market trends.

What are the potential drawbacks of using partially-ordered preferences in decision-making

Potential drawbacks of using partially-ordered preferences in decision-making include:

Complexity: Managing partially-ordered preferences can be more complex than using total orders, leading to challenges in decision-making processes.

Ambiguity: Incomparable outcomes in partially-ordered preferences can introduce ambiguity and uncertainty, making it difficult to determine the best course of action.

Computational Complexity: Computing optimal solutions with partially-ordered preferences may require more computational resources and time compared to total orders.

Interpretation: Interpreting and understanding partially-ordered preferences may be challenging for users and decision-makers, leading to potential misinterpretations and suboptimal decisions.

How can the concept of stochastic ordering be applied in other computational models or systems

The concept of stochastic ordering can be applied in various computational models or systems, such as:

Risk Management: Stochastic ordering can be used to rank and compare risk levels in financial portfolios, insurance policies, and investment strategies.

Quality Control: Stochastic ordering can help in comparing the quality of products or services based on probabilistic measures, ensuring consistency and reliability.

Resource Allocation: Stochastic ordering can assist in prioritizing resource allocation in project management, scheduling, and optimization based on probabilistic criteria.

Environmental Modeling: Stochastic ordering can be applied in environmental modeling to compare and rank different scenarios or policies based on their impact and uncertainty levels.