Konstantinos Sfikas

Design Space Exploration of Shell Structures Using Quality Diversity Algorithms

Abstract:

“Computer-aided optimization algorithms in structural engineering have historically focused on the structural performance of generated forms, often resulting in the selection of a single ‘optimal’ solution. However, diversity of generated solutions is desirable when those solutions are shown to a human user to choose from. Quality-Diversity (QD) search is an emerging field of Evolutionary Computation which can automate the exploration of the solution space in engineering problems. QD algorithms, such as MAP-Elites, operate by maintaining and expanding an archive of diverse solutions, optimising for quality in local niches of a multidimensional design space. The generated archive of solutions can help engineers gain a better overview of the solution space, illuminating which designs are possible and their trade-offs. In this paper we apply Quality Diversity search to the problem of designing shell structures. Since the design of shell structures comes with physical constraints, we leverage a constrained optimization variant of the MAP-Elites algorithm, FI-MAP-Elites. We implement our proposed methodology within the Rhino/Grasshopper environment and use the Karamba Finite Element Analysis solver for all structural engineering calculations. We test our method on case studies of parametric models of shell structures that feature varying complexity. Our experiments investigate the algorithm’s ability to illuminate the solution space and generate feasible and high-quality solutions.”

Citation:

Konstantinos Sfikas, Antonios Liapis, Joel Hilmersson, Jeg Dudley, Edoardo Tibuzzi and Georgios N. Yannakakis: “Design Space Exploration of Shell Structures Using Quality Diversity Algorithms,” in Proceedings of the International Association for Shell and Spatial Structures Symposium, 2023.

BibTeX:

@inproceedings{sfikas2023shell,
    author={Konstantinos Sfikas and Antonios Liapis and Joel Hilmersson and Jeg Dudley and Edoardo Tibuzzi and Georgios N. Yannakakis},
    title={Design Space Exploration of Shell Structures Using Quality Diversity Algorithms},
    booktitle={Proceedings of the International Association for Shell and Spatial Structures Symposium},
    year={2023},
}

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Konstantinos Sfikas In publications

Controllable Exploration of a Design Space via Interactive Quality Diversity

Abstract:

“This paper introduces a user-driven evolutionary algorithm based on Quality Diversity (QD) search. During a design session, the user iteratively selects among presented alternatives and their selections affect the upcoming results. We implement a variation of the MAP-Elites algorithm where the presented alternatives are sampled from a small region (window) of the behavioral space. After a user selection, the window is centered on the selected individual’s behavior characterization, evolution selects parents from within this window to produce offspring, and new alternatives are sampled. Essentially we define an adaptive system of local QD search, where the user’s selections guide the search towards specific regions of the behavioral space. The system is tested on the generation of architectural layouts, a constrained optimization task, leveraging QD search through a two-archive approach.”

Citation:

Konstantinos Sfikas, Antonios Liapis and Georgios N. Yannakakis: “Controllable Exploration of a Design Space via Interactive Quality Diversity,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2023

Bibtex:

@inproceedings{sfikas2023controllable,
    author={Konstantinos Sfikas and Antonios Liapis and Georgios N. Yannakakis},
    title={Controllable Exploration of a Design Space via Interactive Quality Diversity},
    booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion},
    year={2023},
}

A General-Purpose Expressive Algorithm for Room-based Environments

Abstract:

“This paper presents a generative architecture for general-purpose room layouts that can be treated as geometric definitions of dungeons, mansions, shooter levels and more. The motivation behind this work is to provide a design tool for virtual environments that combines aspects of controllability, expressivity and generality. Towards that end, a two-tier level representation is realized, with a graph-based design specification constraining and guiding the generated geometries, facilitated by constrained evolutionary search. Expressivity is secured through quality-diversity search which can provide the designer with a broad variety of level layouts to choose from. Finally, the generator is general-purpose as it can produce layouts based on different types of static grid structures or as freeform, curved structures through an adaptive Voronoi diagram that is evolved along with the level itself. The method is tested on a variety of design specifications and grid types, and results show that even with complex design constraints or malleable grids the algorithm can produce a broad variety of levels.”

Citation:

Konstantinos Sfikas, Antonios Liapis and Georgios N. Yannakakis: “A General-Purpose Expressive Algorithm for Room-based Environments,” in Proceedings of the FDG workshop on Procedural Content Generation, 2022.

Bibtex:

@inproceedings{sfikas2022general,
    author={Konstantinos Sfikas and Antonios Liapis and Georgios N. Yannakakis},
    title={A General-Purpose Expressive Algorithm for Room-based Environments},
    booktitle={Proceedings of the FDG workshop on Procedural Content Generation},
    year={2022},
}

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Konstantinos Sfikas In publications

Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem

Abstract:

“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.”

Citation:

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

@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},
}

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Konstantinos Sfikas In publications

Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic

Abstract:

“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.“

Citation:

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

Bibtex:

@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},
}

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Konstantinos Sfikas In publications

Collaborative Agent Gameplay in the Pandemic Board Game

Abstract:

“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.”

Citation:

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

Bibtex:

@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}, 
}

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Konstantinos Sfikas In publications

Fusing Level and Ruleset Features for Multimodal Learning of Gameplay Outcomes

Abstract:

“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.”

Citation:

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.

BibTeX:

@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},
}

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Konstantinos Sfikas In Games, Global Game Jam

IO – Global Game Jam 2019

Calling it a game might be an overstatement. It is, however, a self contained, playable “thing”, revolving around spatial exploration. It borrows the mechanics of a typical third person game and combines them with an abstract spatial representation and an unclear overall goal, bringing it quite close to a “walking simulator”.

Konstantinos Sfikas In c#, tutorials

Using events in c#

“Events enable a class or object to notify other classes or objects when something of interest occurs. The class that sends (or raises) the event is called the publisher and the classes that receive (or handle) the event are called subscribers.” (source: https://docs.microsoft.com/en-us/dotnet/csharp/programming-guide/events/ )

The use of events in c# is pretty straightforward. However, same as every other programming technique, it may take some time for one to get used to and use effectively. Lately I have been revisiting the use of events in c#, trying to make sure that I use them properly in my projects. While doing so, I made a small case study, in the form of a console application, and decided to share it, in case it might help someone else as well.

Konstantinos Sfikas In publications

A Study on Affect Model Validity: Nominal vs Ordinal Labels

Abstract:

“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.”

Citation:

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.

BibTeX:

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},
}

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