@article {pub4220,
	title = {Action Representation for Intelligent Agents using Memory Nets},
	author = {Julian Eggert AND J{\"o}rg Deigm{\"o}ller AND Lydia Fischer AND Andreas Richter},
	year = {2020},
	abstract = {Memory Nets (Eggert et al.: Memory Nets: Knowledge Rep-
resentation for Intelligent Agent Operations in Real World, 2019) are
a knowledge representation schema targeted at autonomous Intelligent
Agents (IAs) operating in real world. Memory Nets are targeted at lever-
aging the large body of openly available semantic information, and incre-
mentally accumulating additional knowledge from situated interaction.
Here we extend the Memory Net concepts by action representation. In the
first part of this paper, we recap the basic domain independent features of
Memory Nets and the relation to measurements and actuator capabilities
as available by autonomous entities. In the second part we show how the
action representation can be created using the concepts of Memory Nets
and relate actions that are executable by an IA with tools, objects and
the actor itself. Further, we show how action specific information can
be extracted and inferred from the created graph. The combination of
the two main parts provide an important step towards a knowledge base
framework for researching how to create IAs that continuously expand
their knowledge about the world.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	journal = {Communications in Computer and Information Science}
}