TY - GEN
T1 - Open the Chests
T2 - 31st International Symposium on Temporal Representation and Reasoning, TIME 2024
AU - Stoyanova, Ivelina
AU - Museux, Nicolas
AU - Nguyen, Sao Mai
AU - Filliat, David
N1 - Publisher Copyright:
© Ivelina Stoyanova, Nicolas Museux, Sao Mai Nguyen, and David Filliat.
PY - 2024/10/22
Y1 - 2024/10/22
N2 - This article presents Open the Chests, a novel benchmark environment designed for simulating and testing activity recognition and reactive decision-making algorithms. By leveraging temporal logic, Open the Chests offers a dynamic, event-driven simulation platform that illustrates the complexities of real-world systems. The environment contains multiple chests, each representing an activity pattern that an interacting agent must identify and respond to by pressing a corresponding button. The agent must analyze sequences of asynchronous events generated by the environment to recognize these patterns and make informed decisions. With the aim of theoretically grounding the environment, the Activity-Based Markov Decision Process (AB-MDP) is defined, allowing to model the context-dependent interaction with activities. Our goal is to propose a robust tool for the development, testing, and bench-marking of algorithms that is illustrative of realistic scenarios and allows for the isolation of specific complexities in event-driven environments.
AB - This article presents Open the Chests, a novel benchmark environment designed for simulating and testing activity recognition and reactive decision-making algorithms. By leveraging temporal logic, Open the Chests offers a dynamic, event-driven simulation platform that illustrates the complexities of real-world systems. The environment contains multiple chests, each representing an activity pattern that an interacting agent must identify and respond to by pressing a corresponding button. The agent must analyze sequences of asynchronous events generated by the environment to recognize these patterns and make informed decisions. With the aim of theoretically grounding the environment, the Activity-Based Markov Decision Process (AB-MDP) is defined, allowing to model the context-dependent interaction with activities. Our goal is to propose a robust tool for the development, testing, and bench-marking of algorithms that is illustrative of realistic scenarios and allows for the isolation of specific complexities in event-driven environments.
KW - Activity Recognition
KW - Benchmark Environment
KW - Complex Event Processing
KW - Dynamic Systems
KW - Event-Based Decision Making
KW - Real-Time Simulation
KW - Reinforcement Learning
KW - Temporal Logic
U2 - 10.4230/LIPIcs.TIME.2024.5
DO - 10.4230/LIPIcs.TIME.2024.5
M3 - Conference contribution
AN - SCOPUS:85208645555
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 31st International Symposium on Temporal Representation and Reasoning, TIME 2024
A2 - Sala, Pietro
A2 - Sioutis, Michael
A2 - Wang, Fusheng
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Y2 - 28 October 2024 through 30 October 2024
ER -