Finding Attack Strategies for Predator Swarms Using Genetic Algorithms

Leigh, Ryan E.; Morelli, Tony; Louis, Sushil J.; Nicolescu, Mircea; Miles, Christopher E.

doi:10.1109/cec.2005.1554997

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…Similar approaches are reported in Bahgeçi and Sahin (2005); Gauci, Chen, Dodd, and Groß (2014); Soysal, Bahçeci, and S ¸ahin (2007). Evolutionary algorithms have also been applied to synthesize other types of collective behaviors, such as foraging (Bahaidarah, Marjanovic, Rekabi-Bana, & Arvin, 2023;Hecker, Letendre, Stolleis, Washington, & Moses, 2012;Hiraga, Wei, & Ohkura, 2021;Pagliuca, Inglese, & Vitanza, 2023;Pagliuca, Milano, & Nolfi, 2020;Pagliuca & Nolfi, 2019;Pagliuca & Vitanza, 2023b;Witkowski & Ikegami, 2016), coordination (Baldassarre et al, 2007;Shibata & Fukuda, 1993;Sperati, Trianni, & Nolfi, 2008;Trianni, Nolfi, & Dorigo, 2006), formation (Roy, Maitra, & Bhattacharya, 2016;Stolfi & Danoy, 2022, anti-predator strategies (Kunz, Züblin, & Hemelrijk, 2006;Pagliuca & Vitanza, 2023b) or group predation (Leigh, Morelli, Louis, Nicolescu, & Miles, 2005;Von Moll, Androulakakis, Fuchs, & Vanderelst, 2020). In Sperati et al (2008) a genetic algorithm is employed to evolve coordinated behaviors in a group of three E-puck robots (Mondada et al, 2009).…”

Section: Introductionmentioning

confidence: 68%

Exploring Behavioral Diversity in Robot Swarms: A Comparative Study of Evolutionary Strategies for Aggregation Tasks

Pagliuca,

Vitanza

2024

Preprint

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Collective decision-making is widely observed in natural organisms, especially insects and animals. In this regard, aggregation represents one of the paramount behaviors, as it can be useful for protecting groups against predators or speeding up the foraging process. In the field of autonomous robotics, aggregation is modeled through a series of alternative paradigms, among which evolutionary algorithms are considered a convenient tool. In this work, we compared three modern evolutionary strategies --- CMA-ES, xNES and OpenAI-ES --- for their ability to evolve an aggregation behavior in a swarm of robots. Specifically, we systematically varied the number of agents in the group, the environmental conditions (i.e., the number of target nests) and the parameters tuning the fitness function. Our aim is to verify whether and how the selected methods are effective at addressing the problem. The results we obtained indicate how the OpenAI-ES achieves better performance in all the considered scenarios. Furthermore, it displays qualitatively more interesting strategies than the other two methods.

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Section: Introductionmentioning

confidence: 68%

Exploring Behavioral Diversity in Robot Swarms: A Comparative Study of Evolutionary Strategies for Aggregation Tasks

Pagliuca,

Vitanza

2024

Preprint

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“…Whereas Figure 5 is an example of an attack in group action produced when P1 = 0.53, P2 = 0.57, P3 = 1, P4 = 0.08, P5 = 0 and P6 = 1. The first test was conducted by comparing training time between the L-GCNN and NN methods [1]. Table 2 show the test results where NN training time is 33% faster than L-GCNN because L-GCNN has one neuron for each training data in the hidden layer where NN have fewer neurons in the hidden layer.…”

Section: New Smoothing Parametermentioning

confidence: 99%

“…Since the emergence of the ideas about artificial intelligence, games are one of the items that helped advance AI research [1]. Games not only cause interesting and complex problems for AI to solve, they also provide land for creativity and expression for users [2].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Non Playable Character in RPG Game Using Logarithmic Learning For Generalized Classifier Neural Network (L-GCNN)

Mabruroh¹,

Herumurti²

2019

KINETIK

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Non-playable Character (NPC) is one of the important characters in the game. An autonomous and adaptive NPC can adjust actions with player actions and environmental conditions. To determine the actions of the NPC, the previous researchers used the Neural Network method but there were weaknesses, namely the action produced was not in accordance with the desired so the accuracy was not good. This study overcomes the problem of poor accuracy by using the Logarithmic Learning for Generalized Classifier Neural Network (L-GCNN) method with 6 input parameters, NPC health, distance from players, other NPCs involved, attack power, number of NPCs and NPC levels. While the output is to attack itself, attack in groups and move away. For testing, this study was tested on RPG games. From the results of the experiments conducted, it shows that the L-GCNN method has better accuracy than the 3 methods compared to 7% better than NN and SVM and 8% better than RBFNN because in the L-GCNN method there is an encapsulation process that is data have the same class will. Whereas the L-GCNN training time is 30% longer than the NN method because on L-GCNN one neuron consists of one data where there are fewer NNs in the hidden layer.

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“…Fifteen of the top twenty rated games of 2007 are real-time continuous state games [7], and of the top ten selling games of 2007 half are realtime continuous state games [11]. Second, we have experience working in the domain of these games [12,13]. Third, real-time continuous state games have not been explored for research as much as turn-based discrete games [6,14,1].…”

Section: Adaptive Agentsmentioning

confidence: 99%

Using coevolution to understand and validate game balance in continuous games

Leigh

Schonfeld

Louis

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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We attack the problem of game balancing by using a coevolutionary algorithm to explore the space of possible game strategies and counter strategies. We define balanced games as games which have no single dominating strategy. Balanced games are more fun and provide a more interesting strategy space for players to explore. However, proving that a game is balanced mathematically may not be possible and industry commonly uses extensive and expensive human testing to balance games. We show how a coevolutionary algorithm can be used to test game balance and use the publicly available continuous state, capture-the-flag CaST game as our testbed. Our results show that we can use coevolution to highlight game imbalances in CaST and provide intuition towards balancing this game. This aids in eliminating dominating strategies, thus making the game more interesting as players must constantly adapt to opponent strategies.

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Finding Attack Strategies for Predator Swarms Using Genetic Algorithms

Cited by 4 publications

References 6 publications

Exploring Behavioral Diversity in Robot Swarms: A Comparative Study of Evolutionary Strategies for Aggregation Tasks

Exploring Behavioral Diversity in Robot Swarms: A Comparative Study of Evolutionary Strategies for Aggregation Tasks

Adaptive Non Playable Character in RPG Game Using Logarithmic Learning For Generalized Classifier Neural Network (L-GCNN)

Using coevolution to understand and validate game balance in continuous games

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