Szczegóły publikacji

Opis bibliograficzny

Autorzy (3)

• Starzyk Janusz A.
• Kowalik Marcin
• AGHHorzyk Adrian

Dane bibliometryczne

ID BaDAP	149128
Data dodania do BaDAP	2023-11-06
Tekst źródłowy	URL
DOI	10.3233/FAIA230517
Rok publikacji	2023
Typ publikacji	materiały konferencyjne (aut.)
Otwarty dostęp
Creative Commons
Wydawca	IOS Press
Konferencja	26th European Conference on Artificial Intelligence
Czasopismo/seria	Frontiers in Artificial Intelligence and Applications

Abstract

This paper introduces a motivated agent scheme that enables an agent to create its own goals using prior knowledge about its environment. A motivated agent operates in a dynamically changing environment and is capable of setting and achieving its own goals, as well as those set by the designer. The agent has access to additional knowledge about the environment, which is represented in associative semantic memory. This memory is constructed based on ANAKG associative knowledge graphs, which have been shown to have several advantages over other semantic memories for processing symbolic sequential inputs. They are easy to organize and train. In this paper, we demonstrate that a motivated agent with semantic memory learns to achieve its goals more easily and utilizes environmental resources more effectively. To simplify comparisons with reinforcement learning, we represent the environment as an environmental graph that shows the principles governing it. By exploring the environment, the agent learns these principles and can use them to accomplish its tasks. Our experiments and tests confirm our claims about the higher efficiency of agents with memory. Moreover, an extensive comparison with reinforcement learning agents highlights the advantages of motivated learning over reinforcement learning.

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