9 research outputs found
Rolling Horizon NEAT for General Video Game Playing
This paper presents a new Statistical Forward Planning (SFP) method, Rolling
Horizon NeuroEvolution of Augmenting Topologies (rhNEAT). Unlike traditional
Rolling Horizon Evolution, where an evolutionary algorithm is in charge of
evolving a sequence of actions, rhNEAT evolves weights and connections of a
neural network in real-time, planning several steps ahead before returning an
action to execute in the game. Different versions of the algorithm are explored
in a collection of 20 GVGAI games, and compared with other SFP methods and
state of the art results. Although results are overall not better than other
SFP methods, the nature of rhNEAT to adapt to changing game features has
allowed to establish new state of the art records in games that other methods
have traditionally struggled with. The algorithm proposed here is general and
introduces a new way of representing information within rolling horizon
evolution techniques.Comment: 8 pages, 5 figures, accepted for publication in IEEE Conference on
Games (CoG) 202
Collaborative agent gameplay in the Pandemic board game
While artificial intelligence has been applied to control players’
decisions in board games for over half a century, little attention
is given to games with no player competition. Pandemic is an exemplar collaborative board game where all players coordinate to
overcome challenges posed by events occurring during the game’s
progression. This paper proposes an artificial agent which controls
all players’ actions and balances chances of winning versus risk
of losing in this highly stochastic environment. The agent applies
a Rolling Horizon Evolutionary Algorithm on an abstraction of
the game-state that lowers the branching factor and simulates the
game’s stochasticity. Results show that the proposed algorithm
can find winning strategies more consistently in different games
of varying difficulty. The impact of a number of state evaluation
metrics is explored, balancing between optimistic strategies that
favor winning and pessimistic strategies that guard against losing.peer-reviewe
MAPiS 2019 - First MAP-i Seminar: proceedings
This book contains a selection of Informatics papers accepted for presentation and discussion at “MAPiS 2019 - First MAP-i
Seminar”, held in Aveiro, Portugal, January 31, 2019. MAPiS is the first conference organized by the MAP-i first year students,
in the context of the Seminar course. The MAP-i Doctoral Programme in Computer Science is a joint Doctoral Programme in
Computer Science of the University of Minho, the University of Aveiro and the University of Porto. This programme aims to
form highly-qualified professionals, fostering their capacity and knowledge to the research area.
This Conference was organized by the first grade students attending the Seminar Course. The aim of the course was to introduce
concepts which are complementary to scientific and technological education, but fundamental to both completing a PhD
successfully and entailing a career on scientific research. The students had contact with the typical procedures and difficulties of
organizing and participate in such a complex event. These students were in charge of the organization and management of all the
aspects of the event, such as the accommodation of participants or revision of the papers. The works presented in the Conference
and the papers submitted were also developed by these students, fomenting their enthusiasm regarding the investigation in the
Informatics area. (...)publishe