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Bosch, M.P.C.

Cannas, S.A.

Hadzibeganovic, T.

Jonsson, G.K.

Magnusson, M.S.

Noort, M.W.M.L. van den

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The missing neurocognitive and artificial general intelligence bases of robocup reasearch: What still needs to be done before 2050?

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The Missing Neurocognitive and
Artificial General Intelligence Bases of
RoboCup Research: What still needs to
be done before 2050?
Tarik Hadzibeganovic1, Maurits van den Noort2, Sergio A. Cannas3,
Peggy Bosch4, Gudberg K. Jonsson5, and Magnus S. Magnusson5
1 Language Development & Cognitive Science Unit, University of Graz
Heinrichstr. 36, 2. OG; A-8010 Graz, Austria
E-mail: ta.hadzibeganovic@uni-graz.at
2 Department of Biological and Medical Psychology, University of Bergen
Jonas Lies vei 91, N-5009 Bergen, Norway
3 Facultad de Matema´tica, Astronomı´a y F´ısica, Universidad Nacional de Co´rdoba
Ciudad Universitaria, 5000 Co´rdoba, Argentina
4 Nijmegen Institute for Cognition & Information, Radboud University Nijmegen
Montessorilaan 3, 6500 Nijmegen, The Netherlands
5 Human Behavior Laboratory, University of Iceland
Hringbraut 121, IS-101 Reykjavik, Iceland
Abstract
There has been much recent interest in cognitively motivated paradigms
for the design of humanoid soccer playing robots. In addition, some
proto-attempts have been made to bring the 3D simulation and humanoid
RoboCup leagues closer to each other. In the present paper, we outline
the basic concepts of our Neurocognitive Artificial General Intelligence
(NAGI) framework for the future 3D Soccer Simulation League. The aim
of NAGI is to develop teachable AI soccer playing agents embodied in vir-
tual environments such as Second Life. Once virtually embodied agents
attain approximately human-like intelligence, the transfer of agent mod-
eling knowledge to the humanoid robots should take place.
Keywords: Neurocognitive, Artificial General Intelligence, teachable AI
agents, 3D Simulation League, Humanoid League, Novamente Cognition
Engine, Second Life, t-pattern detection.
1 Introduction
Recently there has been much interest in cognitively based paradigms for the
design of humanoid soccer playing robots [1], [2], [3]. In addition, some proto-
attempts have been made to bring the 3D simulation [4], [5] and humanoid [6]
RoboCup leagues closer to each other with an objective to provide new insights
and tools that would speed up the progress across both research areas [7]. In
the present paper, we aim at re-defining the roadmap towards the ultimate goal
of RoboCup research: To build a team of fully autonomous, humanoid soccer
playing artificial agents capable of winning against a human world cup champion
soccer team by 2050.
2 An Integrative Neurocognitive AGI Approach
We argue that this goal is reachable only via a successful integration of human-
like neuro-cognitive architectures, adequate knowledge representation systems
and feasible learning algorithms, or in other words, embodied and smarter-than-
human Artificial General Intelligence (AGI) [8], [9]. The key ideas we propose
here are as follows. First, we outline the basic concepts of our Neurocognitive
Artificial General Intelligence (NAGI) design principle for the future 3D Soccer
Simulation League. Here, we argue that the core of the design is representable
through approaches such as the Novamente Cognition Engine [10], [11], [12],
employing two distinct cognitive algorithms – probabilistic term logic (PTL)
and Bayesian optimization algorithm (BOA) [12], [13], [14], [15].
3 Virtually Embodied Soccer Playing Agents
The main goal of NAGI is to develop teachable AI soccer playing agents living
in virtual environments such as Second Life (SL). In a human-like manner, these
agents would ascent through different stages of cognitive development, by inter-
acting with both human avatars (users of SL) and artificial autonomous agents
in virtual worlds. Once virtually embodied soccer playing agents attain approx-
imately human-level intelligence, the transfer of agent modeling knowledge to
the humanoid robots should take place. Thus, the major point of this proposal
is that virtual worlds are an excellent benchmark for testing and maturation of
AGI [11] necessary for the development of both digital and in silico human-like
soccer players.
4 Alternative Approaches
The realization of our goal is not restricted to the outlined design; other candi-
date approaches that have the potential to develop AGI, such as adiabatic quan-
tum computing [16], or emergent, content-addressable and associative quantum
holographic neural-like network systems [17] and their recent implementations
in humanoid robotics [18], are also discussed.
5 Future Directions
Since RoboCup research community aims at developing artificial agents that
would be able to mimic human behavioral patterns during soccer games, there
is a need for measuring and comparing the emerging behavior across populations
(human vs. artificial). Moreover, it is important to develop and standardize a
particular pattern detection system that could be used by all research groups and
that would further serve as a measure of efficiency of research improvements. For
this purpose, we suggest the t-pattern detection approach [19], [20], which has
already been successfully applied to the analyses of human-animal and human-
robot interactions [21], and real-life human soccer matches [22], [23].
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The missing neurocognitive and artificial general intelligence bases of robocup reasearch: What still needs to be done before 2050?

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