AutoMoDe-Arlequin: Neural Networks as Behavioral Modules for the Automatic Design of Probabilistic Finite-State Machines

2020 ◽  
pp. 271-281
Author(s):  
Antoine Ligot ◽  
Ken Hasselmann ◽  
Mauro Birattari
Keyword(s):  
Neural Networks ◽  
Finite State Machines ◽  
Automatic Design ◽  
State Machines ◽  
Finite State

scholarly journals Automatic modular design of robot swarms using behavior trees as a control architecture

2020 ◽  
Vol 6 ◽  
pp. e314
Author(s):  
Antoine Ligot ◽  
Jonas Kuckling ◽  
Darko Bozhinoski ◽  
Mauro Birattari
Keyword(s):  
Swarm Robotics ◽  
Modular Design ◽  
Design Method ◽  
Finite State Machines ◽  
Control Architecture ◽  
Automatic Design ◽  
State Machines ◽  
Collective Behaviors ◽  
Behavior Trees ◽  
Finite State

We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules—low-level behaviors and conditions—into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: aggregation and Foraging. To explore the strengths and weaknesses of adopting behavior trees as a control architecture, we compare Maple with Chocolate, a previously proposed automatic design method that uses probabilistic finite state machines instead. In the first study, we assess Maple’s ability to produce control software that crosses the reality gap satisfactorily. In the second study, we investigate Maple’s performance as a function of the design budget, that is, the maximum number of simulation runs that the design process is allowed to perform. In the third study, we explore a number of possible variants of Maple that differ in the constraints imposed on the structure of the behavior trees generated. The results of the three studies indicate that, in the context of swarm robotics, behavior trees might be appealing but in many settings do not produce better solutions than finite state machines.

scholarly journals An Algebraic Framework to Represent Finite State Machines in Single-Layer Recurrent Neural Networks

1995 ◽  
Vol 7 (5) ◽  
pp. 931-949 ◽  
Author(s):  
R. Alquézar ◽  
A. Sanfeliu
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Single Layer ◽  
Finite State Machines ◽  
Grammatical Inference ◽  
State Machines ◽  
Linear System Of Equations ◽  
Wide Range ◽  
Finite State ◽  
Algebraic Framework

In this paper we present an algebraic framework to represent finite state machines (FSMs) in single-layer recurrent neural networks (SLRNNs), which unifies and generalizes some of the previous proposals. This framework is based on the formulation of both the state transition function and the output function of an FSM as a linear system of equations, and it permits an analytical explanation of the representational capabilities of first-order and higher-order SLRNNs. The framework can be used to insert symbolic knowledge in RNNs prior to learning from examples and to keep this knowledge while training the network. This approach is valid for a wide range of activation functions, whenever some stability conditions are met. The framework has already been used in practice in a hybrid method for grammatical inference reported elsewhere (Sanfeliu and Alquézar 1994).

Time-delay neural networks: representation and induction of finite-state machines

1997 ◽  
Vol 8 (5) ◽  
pp. 1065-1070 ◽  
Author(s):  
D.S. Clouse ◽  
C.L. Giles ◽  
B.G. Horne ◽  
G.W. Cottrell
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Finite State Machines ◽  
State Machines ◽  
Finite State
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