On the Design of Boolean Network Robots

Roli, Andrea; Manfroni, Mattia; Pinciroli, Carlo; Birattari, Mauro

doi:10.1007/978-3-642-20525-5_5

Cited by 53 publications

(30 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Instances of POBDSs abound in fields such as genomics [12], robotics [13], digital communication systems [14], and more. The optimal recursive minimum mean-square error (MMSE) state estimators for this model are called the Boolean Kalman Filter (BKF) [1] and the Boolean Kalman Smoother (BKS) [15].…”

Section: Introductionmentioning

confidence: 99%

Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

Imani

Braga-Neto

2017

IEEE Trans. Signal Process.

104

View full text Add to dashboard Cite

Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear models with application in estimation and control of Boolean processes based on noisy and incomplete measurements. The optimal minimum mean square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the case of large systems, due to computational and memory requirements. To address this, we propose approximate MMSE filtering and smoothing algorithms based on the auxiliary particle filter (APF) method from sequential Monte-Carlo theory. These algorithms are used jointly with maximum-likelihood (ML) methods for simultaneous state and parameter estimation in POBDS models. In the presence of continuous parameters, ML estimation is performed using the expectation-maximization (EM) algorithm; we develop for this purpose a special smoother which reduces the computational complexity of the EM algorithm. The resulting particle-based adaptive filter is applied to a POBDS model of Boolean gene regulatory networks observed through noisy RNA-Seq time series data, and performance is assessed through a series of numerical experiments using the well-known cell cycle gene regulatory model.

show abstract

Section: Introductionmentioning

confidence: 99%

Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

Imani

Braga-Neto

2017

IEEE Trans. Signal Process.

104

View full text Add to dashboard Cite

show abstract

“…Results are shown in Figure 2. As we can observe, cluster {1, 3,5,8,10,12,18} has a higher set- 3,5,7,8,10,12,18} compared with the set-influence of random clusters of the same size (averaged over 100 random clusters)…”

Section: Networkmentioning

confidence: 93%

“…BNs are extremely interesting from an engineering perspective because of their ability to display complex and rich dynamics, despite the compactness of their description. In a previous work, it has been shown that BNs can be used to control robots [12].…”

Section: Bn-robot Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Identification of Dynamical Structures in Artificial Brains: An Analysis of Boolean Network Controlled Robots

Roli

Villani

Serra

et al. 2013

AI*IA 2013: Advances in Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

Abstract. Automatic techniques for the design of artificial computational systems, such as control programs for robots, are currently achieving increasing attention within the AI community. A prominent case is the design of artificial neural network systems by means of search techniques, such as genetic algorithms. Frequently, the search calibrates not only the system parameters, but also its structure. This procedure has the advantage of reducing the bias introduced by the designer and makes it possible to explore new, innovative solutions. The drawback, though, is that the analysis of the resulting system might be extremely difficult and limited to few coarse-grained characteristics. In this paper, we consider the case of robots controlled by Boolean networks that are automatically designed by means of a training process based on local search. We propose to analyse these systems by a method that detects mesolevel dynamical structures. These structures are emerging patterns composed of elements that behave in a coherent way and loosely interact with the rest of the system. In general, this method can be used to detect functional clusters and emerging structures in nonlinear discrete dynamical systems. It is based on an extension of the notion of cluster index, which has been previously proposed by Edelman and Tononi to analyse biological neural systems. Our results show that our approach makes it possible to identify the computational core of a Boolean network which controls a robot.

show abstract

“…The engineering-oriented applications of Boolean networks in the literature include biomedical applications such as modeling and potentially influencing the progression of a cellular-level disease (Datta et al, 2003;Datta et al, 2004;Pal et al, 2006;Shmulevich et al, 2002b). There are also applications in artificial intelligence and robotics where Boolean networks are used as a decision-making or dedicated control subsystem (Bongard, 2011;Garattoni et al, 2013;Paczuski et al, 2000;Roli et al, 2011;Vichi et al, 2012). In both of these application types, the use of Boolean networks is limited to modeling the target system as a Boolean network or using a Boolean network as a foundational component in the design of a system.…”

Section: Introductionmentioning

confidence: 99%

Influencing Message Propagation in a Social Network Using Embedded Boolean Networks: A Demonstration Using Agent‐Based Modeling

Polacek

Verma

Yang

2016

INCOSE International Symp

View full text Add to dashboard Cite

A Boolean network is a self‐organizing construct originally developed in the field of theoretical biology to investigate the limited variety of processes within genetic networks. Our research objective is to reformulate this descriptive biological/mathematical model into an engineering approach that can be used to shape the operational state space of a targeted complex system in a desired way. We demonstrate this by coupling the state control of a Boolean network to the rules used by the agents in an agent‐based system, thereby controlling when these rules are expressed. The application presented in this paper uses an agent‐based model of the financial community network on Twitter as the target system. A Boolean network is employed to influence the propagation of information through the community by inhibiting the retweeting rule of agents with high network centrality. The results are compared against those produced by manually removing the high centrality nodes.

show abstract

On the Design of Boolean Network Robots

Cited by 53 publications

References 21 publications

Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

Identification of Dynamical Structures in Artificial Brains: An Analysis of Boolean Network Controlled Robots

Influencing Message Propagation in a Social Network Using Embedded Boolean Networks: A Demonstration Using Agent‐Based Modeling

Contact Info

Product

Resources

About