Comparison of different cooperation strategies in the prey-predator problem

Gesù, Vito Di; Lenzitti, Biagio; Bosco, Giosuè Lo; Tegolo, Domenico

doi:10.1109/camp.2007.4350364

Cited by 10 publications

(9 citation statements)

References 14 publications

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“…iii) Run the game for the current step and observe the next state s t+1 . iv) Get the reward, r, from (11). v) From (8), calculate Q ( s t+1 , u ).…”

Section: Algorithm 1 Learning In the Qfismentioning

confidence: 99%

“…In the pursuit-evasion game there are one or several pursuers that attempt to capture one or several evaders in minimal time while the evaders try to escape or to maximize the capturing time [10]. Hence, this problem can be considered as an optimization problem with conflict objectives [11]. In the pursuit-evasion game each player should learn the best action to take at each instant of time to adapt to an uncertain or changing environment.…”

Section: Pursuit-evasion Gamementioning

confidence: 99%

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An investigation of methods of parameter tuning for Q-Learning Fuzzy Inference System

Al-Talabi

Schwartz

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This paper investigates four methods of implementing a Q-Learning Fuzzy Inference System(QFIS) algorithm to autonomously tune the parameters of a fuzzy inference system. We use an actor-critique structure and we simulate mobile robots playing the differential form of the pursuit evasion game. Both the critique and the actor are fuzzy inference systems. The four methods come from the fact whether it is necessary to tune all the parameters (i.e. all the premise and the consequent parameters) of the critique and the actor or just tune their consequent parameters. The four methods are applied to three versions of the pursuit evasion games. In the first version just the pursuer is learning. In the second version, the evader uses its higher maneuverability and plays intelligently against a self-learning pursuer. In the final version, both the pursuer and the evader are learning. We evaluate which parameters are best to tune and which parameters have little impact on the performance.

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“…iii) Run the game for the current step and observe the next state s t+1 . iv) Get the reward, r, from (11). v) From (8), calculate Q ( s t+1 , u ).…”

Section: Algorithm 1 Learning In the Qfismentioning

confidence: 99%

Section: Pursuit-evasion Gamementioning

confidence: 99%

An investigation of methods of parameter tuning for Q-Learning Fuzzy Inference System

Al-Talabi

Schwartz

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Section: Pursuit-evasion Gamementioning

confidence: 99%

“…It was found that the PSO algorithm has similar or better performance than GA [17]. Also, Gesu et al [18] compared the application of PSO and GA methods in experiments with multiple predators and a single prey. Results show the effectiveness of using PSO algorithm to help the predators to capture the prey in minimal time.…”

Section: Introductionmentioning

confidence: 97%

A two stage learning technique using PSO-based FLC and QFIS for the pursuit evasion differential game

Al-Talabi

Schwartz

2014

2014 IEEE International Conference on Mechatronics and Automation

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This paper presents a two stage learning technique that combines a particle swarm optimization (PSO)-based fuzzy logic control (FLC) algorithm with the Q-Learning fuzzy inference system (QFIS) algorithm. The PSO algorithm is used as a global optimizer to autonomously tune the parameters of a fuzzy logic controller. On the other hand, the QFIS algorithm is used as a local optimizer. We simulate mobile robots playing the differential form of the pursuit evasion game. The game is played such that the pursuer should learn its default control strategy on-line by interacting with the evader. We assume that the evader plays a well defined strategy which is to run away along the line of sight. The pursuer's learning process depends on the rewards received from its environment. The proposed technique is compared through simulation with the default control strategy, the PSO-based fuzzy logic control algorithm, and the QFIS algorithm. Simulation results show that the proposed learning technique outperform the PSO-based fuzzy logic control algorithm and the QFIS algorithm with respect to the learning time which represents an important factor in on-line applications.

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“…Related research includes Di Gesu et al [13], who compared PSO and a Genetic Algorithm (GA) in experiments that simulated multiple predators attempting to capture a single prey in the least amount of time. Also, Lee et al [14] compared PSO and a GA for simulating predatorprey dynamics as a means of modeling a genetic regulatory network.…”

Section: Introductionmentioning

confidence: 99%

Neuro-evolution versus Particle Swarm Optimization for competitive co-evolution of pursuit-evasion behaviors

Langenhoven

Nitschke

2010

IEEE Congress on Evolutionary Computation

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This paper presents a study that compares the efficacy of Neuro-Evolution (NE) versus Particle Swarm Optimization (PSO) for evolving Artificial Neural Network (ANN) controllers in an unsupervised adaptation process. The research objective is to ascertain which adaptive method is most appropriate for deriving agent behaviors in a competitive coevolution pursuit-evasion task. This task requires one predator agent to capture one prey agent in a simulation where behavior adaptation is guided by an arms race of competitive coevolution. Results indicate that NE was overall more effective at deriving pursuit and evasion behaviors according to the task performance measures defined for this study.

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Comparison of different cooperation strategies in the prey-predator problem

Cited by 10 publications

References 14 publications

An investigation of methods of parameter tuning for Q-Learning Fuzzy Inference System

An investigation of methods of parameter tuning for Q-Learning Fuzzy Inference System

A two stage learning technique using PSO-based FLC and QFIS for the pursuit evasion differential game

Neuro-evolution versus Particle Swarm Optimization for competitive co-evolution of pursuit-evasion behaviors

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