Generic animats
Paper i proceeding, 2017

We present a computational model for artificial animals (animats) living in block worlds, e.g. in Minecraft. Each animat has its individual sets of needs, sensors, and motors. It also has a memory structure that undergoes continuous development and constitutes the basis for decision-making. The mechanisms for learning and decision-making are generic in the sense that they are the same for all animats. The goal of the decision-making is always the same: to keep the needs as satisfied as possible for as long as possible. All learning is driven by surprise relating to need satisfaction. The learning mechanisms are of two kinds: (i) structural learning that adds nodes and connections to the memory structure; (ii) a local version of multi-objective Q-learning. The animats are autonomous and capable of adaptation to arbitrary block worlds without any need for seed knowledge.

Dynamic graph

Autonomous agent

Need satisfaction

Multi-objective reinforcement learning

Structural learning


Claes Strannegård

Chalmers, Tillämpad informationsteknologi, Interaktionsdesign

N. Svangård

Göteborgs universitet

J. Bach

Harvard University

B. Steunebrink


Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 10414 23-32
978-3-319-63702-0 (ISBN)


Datavetenskap (datalogi)





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