On Verifying and Generating Robust Plans for Planning Tasks with Exogenous Events

Planning and acting under the presence of exogenous events brings a number of challenges as events might modify the environment without the consent of the acting agent. Consequently, the agent's plan might get disrupted, agent's goals might no longer be achievable, or, worse, the agent might suffer some damage (e.g. damage to the robot). Although policies, mapping states to appropriate actions to take, can describe, in theory, how the agent should act, they might be difficult to explain and understand for humans in the loop. In this paper, we describe the concept of robust plans that are sequences of actions that can be successfully executed regardless of event occurrence. Robust plans are easier to understand (than policies). We present two methods for verifying whether a sequence of actions is a robust plan, one based on compilation to classical planning, and the other based on leveraging delete-relaxation. We also present a method for generating robust plans that is derived from the "relaxation" verification method. The methods are evaluated on three domains.

Authors

Institutions

Publication Details

Published
2024-10-26
DOI
https://doi.org/10.24963/kr.2024/26
Primary Topic
AI-based Problem Solving and Planning
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On Verifying and Generating Robust Plans for Planning Tasks with Exogenous Events

Erez Karpas, Lukáš Chrpa
AI-based Problem Solving and Planning
article

On Verifying and Generating Robust Plans for Planning Tasks with Exogenous Events

Erez Karpas, Lukáš Chrpa
article en

Abstract

Planning and acting under the presence of exogenous events brings a number of challenges as events might modify the environment without the consent of the acting agent. Consequently, the agent's plan might get disrupted, agent's goals might no longer be achievable, or, worse, the agent might suffer some damage (e.g. damage to the robot). Although policies, mapping states to appropriate actions to take, can describe, in theory, how the agent should act, they might be difficult to explain and understand for humans in the loop. In this paper, we describe the concept of robust plans that are sequences of actions that can be successfully executed regardless of event occurrence. Robust plans are easier to understand (than policies). We present two methods for verifying whether a sequence of actions is a robust plan, one based on compilation to classical planning, and the other based on leveraging delete-relaxation. We also present a method for generating robust plans that is derived from the "relaxation" verification method. The methods are evaluated on three domains.

Technion – Israel Institute of Technology (IL), Czech Technical University in Prague (CZ)
European Commission, Grantová Agentura České Republiky
Openalex Percentile: Top 83%
AI-based Problem Solving and Planning
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

On Verifying and Generating Robust Plans for Planning Tasks with Exogenous Events — Erez Karpas, Lukáš Chrpa · (2024) | TGRS Research Map | TGRS