Explaining Preference-driven Schedules: the EXPRES Framework

• URL: http://arxiv.org/abs/2203.08895v1
• Date: Wed, 16 Mar 2022 19:15:21 GMT
• Title: Explaining Preference-driven Schedules: the EXPRES Framework
• Authors: Alberto Pozanco, Francesca Mosca, Parisa Zehtabi, Daniele Magazzeni, Sarit Kraus
• Abstract summary: We introduce the EXPRES framework, which can explain why a preference was unsatisfied in a given optimal schedule. The EXPRES framework consists of: (i) an explanation generator that, based on a Mixed-Integer Linear Programming model, finds the best set of reasons that can explain an unsatisfied preference; and (ii) an explanation, which translates the generated explanations into human interpretable ones.
• Score: 23.47188079362728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scheduling is the task of assigning a set of scarce resources distributed over time to a set of agents, who typically have preferences about the assignments they would like to get. Due to the constrained nature of these problems, satisfying all agents' preferences is often infeasible, which might lead to some agents not being happy with the resulting schedule. Providing explanations has been shown to increase satisfaction and trust in solutions produced by AI tools. However, it is particularly challenging to explain solutions that are influenced by and impact on multiple agents. In this paper we introduce the EXPRES framework, which can explain why a given preference was unsatisfied in a given optimal schedule. The EXPRES framework consists of: (i) an explanation generator that, based on a Mixed-Integer Linear Programming model, finds the best set of reasons that can explain an unsatisfied preference; and (ii) an explanation parser, which translates the generated explanations into human interpretable ones. Through simulations, we show that the explanation generator can efficiently scale to large instances. Finally, through a set of user studies within J.P. Morgan, we show that employees preferred the explanations generated by EXPRES over human-generated ones when considering workforce scheduling scenarios.

Related papers

• COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations [89.37527535663433]
  We present a large-scale dataset of 104k posts with user-provided notes and helpfulness labels.<n>We propose a framework that automatically generates and improves reason definitions via automatic prompt optimization.<n>Our experiments show that the optimized definitions can improve both helpfulness and reason prediction.
  arXiv  Detail & Related papers  (2025-10-28T05:28:47Z)
• From Facts to Foils: Designing and Evaluating Counterfactual Explanations for Smart Environments [1.2900933310976797]
  We present the first formalization and implementation of counterfactual explanations tailored to rule-based smart environments.<n>We conducted a user study to evaluate our generated counterfactuals against traditional causal explanations.
  arXiv  Detail & Related papers  (2025-10-03T15:06:53Z)
• GSM-Agent: Understanding Agentic Reasoning Using Controllable Environments [56.007498767771075]
  GSM-Agent is a novel benchmark for evaluating agentic reasoning in complex environments.<n>We analyze the agentic reasoning patterns by cluster the environment's document embeddings into nodes, and map each tool call to its nearest node.<n>We propose a tool-augmented test-time scaling method to improve LLM's agentic reasoning performance by adding tools to encourage models to revisit.
  arXiv  Detail & Related papers  (2025-09-26T07:24:37Z)
• PGPO: Enhancing Agent Reasoning via Pseudocode-style Planning Guided Preference Optimization [58.465778756331574]
  We propose a pseudocode-style Planning Guided Preference Optimization method called PGPO for effective agent learning.<n>With two planning-oriented rewards, PGPO further enhances LLM agents' ability to generate high-quality P-code Plans.<n>Experiments show that PGPO achieves superior performance on representative agent benchmarks and outperforms the current leading baselines.
  arXiv  Detail & Related papers  (2025-06-02T09:35:07Z)
• PixelThink: Towards Efficient Chain-of-Pixel Reasoning [70.32510083790069]
  PixelThink is a simple yet effective scheme that integrates externally estimated task difficulty and internally measured model uncertainty.<n>It learns to compress reasoning length in accordance with scene complexity and predictive confidence.<n> Experimental results demonstrate that the proposed approach improves both reasoning efficiency and overall segmentation performance.
  arXiv  Detail & Related papers  (2025-05-29T17:55:49Z)
• XPrompt:Explaining Large Language Model's Generation via Joint Prompt Attribution [26.639271355209104]
  Large Language Models (LLMs) have demonstrated impressive performances in complex text generation tasks. The contribution of the input prompt to the generated content still remains obscure to humans. We introduce a counterfactual explanation framework based on joint prompt attribution, XPrompt.
  arXiv  Detail & Related papers  (2024-05-30T18:16:41Z)
• Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models [54.21695754082441]
  We propose a framework to teach Large Language Models (LLMs) to generate explainable stock predictions. A reflective agent learns how to explain past stock movements through self-reasoning, while the PPO trainer trains the model to generate the most likely explanations. Our framework can outperform both traditional deep-learning and LLM methods in prediction accuracy and Matthews correlation coefficient.
  arXiv  Detail & Related papers  (2024-02-06T03:18:58Z)
• Getting MoRE out of Mixture of Language Model Reasoning Experts [71.61176122960464]
  We propose a Mixture-of-Reasoning-Experts (MoRE) framework that ensembles diverse specialized language models. We specialize the backbone language model with prompts optimized for different reasoning categories, including factual, multihop, mathematical, and commonsense reasoning. Our human study confirms that presenting expert predictions and the answer selection process helps annotators more accurately calibrate when to trust the system's output.
  arXiv  Detail & Related papers  (2023-05-24T02:00:51Z)
• Explainable Multi-Agent Reinforcement Learning for Temporal Queries [18.33682005623418]
  This work presents an approach for generating policy-level contrastive explanations for MARL to answer a temporal user query. The proposed approach encodes the temporal query as a PCTL logic formula and checks if the query is feasible under a given MARL policy. The results of a user study show that the generated explanations significantly improve user performance and satisfaction.
  arXiv  Detail & Related papers  (2023-05-17T17:04:29Z)
• Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting [80.9896041501715]
  Explanations that have not been "tuned" for a task, such as off-the-shelf explanations written by nonexperts, may lead to mediocre performance. This paper tackles the problem of how to optimize explanation-infused prompts in a blackbox fashion.
  arXiv  Detail & Related papers  (2023-02-09T18:02:34Z)
• Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner [56.08919422452905]
  We propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR) Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises. We outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300% gain in overall correctness.
  arXiv  Detail & Related papers  (2022-05-18T21:52:11Z)
• Task-Agnostic Graph Explanations [50.17442349253348]
  Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph structured data. Existing learning-based GNN explanation approaches are task-specific in training. We propose a Task-Agnostic GNN Explainer (TAGE) trained under self-supervision with no knowledge of downstream tasks.
  arXiv  Detail & Related papers  (2022-02-16T21:11:47Z)
• Not all users are the same: Providing personalized explanations for sequential decision making problems [25.24098967133101]
  This work proposes an end-to-end adaptive explanation generation system. It begins by learning the different types of users that the agent could interact with. It is then tasked with identifying the type on the fly and adjust its explanations accordingly.
  arXiv  Detail & Related papers  (2021-06-23T07:46:19Z)
• Optimal Counterfactual Explanations in Tree Ensembles [3.8073142980733]
  We advocate for a model-based search aiming at "optimal" explanations and propose efficient mixed-integer programming approaches. We show that isolation forests can be modeled within our framework to focus the search on plausible explanations with a low outlier score.
  arXiv  Detail & Related papers  (2021-06-11T22:44:27Z)
• Modelling Multi-Agent Epistemic Planning in ASP [66.76082318001976]
  This paper presents an implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature.
  arXiv  Detail & Related papers  (2020-08-07T06:35:56Z)

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.