Planning and Acting in Dynamic Environments: Identifying and Avoiding Dangerous Situations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00363577" target="_blank" >RIV/68407700:21230/22:00363577 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/22:10437330
Result on the web
<a href="https://doi.org/10.1080/0952813X.2021.1938697" target="_blank" >https://doi.org/10.1080/0952813X.2021.1938697</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/0952813X.2021.1938697" target="_blank" >10.1080/0952813X.2021.1938697</a>
Alternative languages
Result language
angličtina
Original language name
Planning and Acting in Dynamic Environments: Identifying and Avoiding Dangerous Situations
Original language description
In dynamic environments, external events might occur and modify the environment without consent of intelligent agents. Plans of the agents might hence be disrupted and, worse, the agents might end up in dead-end states and no longer be able to achieve their goals. Hence, the agents should monitor the environment during plan execution and if they encounter a dangerous situation they should (reactively) act to escape from it. In this paper, we introduce the notion of dangerous states that the agent might encounter during its plan execution in dynamic environments. We present a method for computing lower bound of dangerousness of a state after applying a sequence of actions. That method is leveraged in identifying situations in which the agent has to start acting to avoid danger. We present two types of such behaviour - purely reactive and proactive (eliminating the source of danger). The introduced concepts for planning with dangerous states are implemented and tested in two scenarios - a simple RPG-like game, called Dark Dungeon, and a platform game inspired by the Perestroika video game. The results show that reasoning with dangerous states achieves better success rate (reaching the goals) than naive planning or rule-based techniques.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Experimental and Theoretical Artificial Intelligence
ISSN
0952-813X
e-ISSN
1362-3079
Volume of the periodical
34
Issue of the periodical within the volume
6
Country of publishing house
GB - UNITED KINGDOM
Number of pages
24
Pages from-to
925-948
UT code for WoS article
000668480700001
EID of the result in the Scopus database
2-s2.0-85109083631