This page lists my peer reviewed publications with links to the full papers, abstracts and citation information.

Konstantinos Sfikas, Antonios Liapis and Georgios N. Yannakakis: “Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem” in Proceedings of the Genetic and Evolutionary Computation Conference, 2021

“A core challenge of evolutionary search is the need to balance between exploration of the search space and exploitation of highly fit regions. Quality-diversity search has explicitly walked this tightrope between a population’s diversity and its quality. This paper extends a popular quality-diversity search algorithm, MAP-Elites, by treating the selection of parents as a multi-armed bandit problem. Using variations of the upper-confidence bound to select parents from under-explored but  potentially rewarding areas of the search space can accelerate the discovery of new regions as well as improve its archive’s total quality. The paper tests an indirect measure of quality for parent selection: the survival rate of a parent’s offspring. Results show that maintaining a balance between exploration and exploitation leads to the most diverse and high-quality set of solutions in three different testbeds.”

@inproceedings{sfikas2021montecarloelites,
    author={Konstantinos Sfikas and Antonios Liapis and Georgios N. Yannakakis},
    title={Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem},
    booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
    year={2021},
}

Konstantinos Sfikas and Antonios Liapis: “Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic” in IEEE Transactions of Games, 2021

“Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling horizon evolutionary algorithm designed specifically for this game. The complex way in which the Pandemic game state changes in a stochastic but predictable way required a number of specially designed forward models, macro-action representations for decision-making, and repair functions for the genetic operations of the evolutionary algorithm. Variants of the algorithm which explore optimistic versus pessimistic game state evaluations, different mutation rates and event horizons are compared against a baseline hierarchical policy agent. Results show that an evolutionary approach via short-horizon rollouts can better account for the future dangers that the board may introduce, and guard against them. Results highlight the types of challenges that collaborative board games pose to artificial intelligence, especially for handling multi-player collaboration interactions.

@article{sfikas2021againstpandemic,
    author={Konstantinos Sfikas and Antonios Liapis},
    title={Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic},
    journal={IEEE Transactions of Games},
    year={2021},
    note={accepted},
}

Konstantinos Sfikas and Antonios Liapis: “Collaborative Agent Gameplay in the Pandemic Board Game” in Proceedings of the Foundations of Digital Games Conference, 2020.

“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.”

@inproceedings{sfikas2020collaborative, 
    author = {Konstantinos Sfikas and Antonios Liapis}, 
    title = {Collaborative Agent Gameplay in the Pandemic Board Game}, 
    booktitle={Proceedings of the Foundations of Digital Games Conference}, 
    year={2020}, 
}

Antonios Liapis, Daniel Karavolos, Konstantinos Makantasis, Konstantinos Sfikas and Georgios N. Yannakakis: “Fusing Level and Ruleset Features for Multimodal Learning of Gameplay Outcomes” in Proceedings of the IEEE Conference on Games, 2019.

“Which features of a game influence the dynamics of players interacting with it? Can a level’s architecture change the balance between two competing players, or is it mainly determined by the character classes and roles that players choose before the game starts? This paper assesses how quantifiable gameplay outcomes such as score, duration and features of the heatmap can be predicted from different facets of the initial game state, specifically the architecture of the level and the character classes of the players. Experiments in this paper explore how different representations of a level and class parameters in a shooter game affect a deep learning model which attempts to predict gameplay outcomes in a large corpus of simulated matches. Findings in this paper indicate that a few features of the ruleset (i.e. character class parameters) are the main drivers for the model’s accuracy in all tested gameplay outcomes, but the levels (especially when processed) can augment the model.”

@inproceedings{liapis2019fusing,
    author = {Antonios Liapis and Daniel Karavolos and Konstantinos Makantasis and Konstantinos Sfikas and Georgios N. Yannakakis},
    title = {Fusing Level and Ruleset Features for Multimodal Learning of Gameplay Outcomes},
    booktitle = {Proceedings of the IEEE Conference on Games},
    year = {2019},
}

David Melhart, Konstantinos Sfikas, Giorgos Giannakakis, Georgios N. Yannakakis and Antonios Liapis: “A Study on Affect Model Validity: Nominal vs Ordinal Labels,” in Proceedings of the IJCAI workshop on AI and Affective Computing, 2018.

“The question of representing emotion computationally remains largely unanswered: popular approaches require annotators to assign a magnitude (or a class) of some emotional dimension, while an alternative is to focus on the relationship between two or more options. Recent evidence in affective computing suggests that following a methodology of ordinal annotations and processing leads to better reliability and validity of the model. This paper compares the generality of classification methods versus preference learning methods in predicting the levels of arousal in two widely used affective datasets. Findings of this initial study further validate the hypothesis that approaching affect labels as ordinal data and building models via preference learning yields models of better validity.”

inproceedings{melhart2018study,
    author={David Melhart and Konstantinos Sfikas and Giorgos Giannakakis and Georgios N. Yannakakis and Antonios Liapis},
    title={A Study on Affect Model Validity: Nominal vs Ordinal Labels},
    booktitle={Proceedings of the IJCAI workshop on AI and Affective Computing},
    year={2018},
}