A Swarm intelligence approach for biometrics verification and identification

In this paper we investigate a swarm intelligence classification approach for both biometrics verification and identification problems. We model the problem by representing biometric templates as ants, grouped in colonies representing the clients of a biometrics authentication system. The biometric template classification process is modeled as the aggregation of ants to colonies. When test input data is captured -- a new ant in our representation -- it will be influenced by the deposited phermonones related to the population of the colonies. We experiment with the Aggregation Pheromone density based Classifier (APC), and our results show that APC outperforms ``traditional'' techniques -- like 1-nearest-neighbour and Support Vector Machines -- and we also show that performance of APC are comparable to several state of the art face verification algorithms. The results here presented let us conclude that swarm intelligence approaches represent a very promising direction for further investigations for biometrics verification and identification

Symmetry Reduction Enables Model Checking of More Complex Emergent Behaviours of Swarm Navigation Algorithms

The emergent global behaviours of robotic swarms are important to achieve their navigation task goals. These emergent behaviours can be verified to assess their correctness, through techniques like model checking. Model checking exhaustively explores all possible behaviours, based on a discrete model of the system, such as a swarm in a grid. A common problem in model checking is the state-space explosion that arises when the states of the model are numerous. We propose a novel implementation of symmetry reduction, in the form of encoding navigation algorithms relatively with respect to a reference, based on the symmetrical properties of swarms in grids. We applied the relative encoding to a swarm navigation algorithm, Alpha, modelled for the NuSMV model checker. A comparison of the state-space and verification results with an absolute (or global) and a relative encoding of the Alpha algorithm highlights the advantages of our approach, allowing model checking larger grid sizes and number of robots, and consequently, verifying more complex emergent behaviours. For example, a property was verified for a grid with 3 robots and a maximum allowed size of 8x8 cells in a global encoding, whereas this size was increased to 16x16 using a relative encoding. Also, the time to verify a property for a swarm of 3 robots in a 6x6 grid was reduced from almost 10 hours to only 7 minutes. Our approach is transferable to other swarm navigation algorithms.Comment: Accepted for presentation in Towards Autonomous Robotic Systems (TAROS) 2015, Liverpool, U

Formal Verification of Pure Production Systems Programs

Reliability, defined as the guarantee that a program satisfies its specifications, is an important aspect of many applications for which rule-based expert systems are suited. Executing rule-based programs on a series of test cases. To show a program is reliable, it is desirable to construct formal specifications for the program and to prove that it obeys those specifications. This paper presents an assertional approach to the verification of a class of rule-based programs characterized by the absence of conflict resolution. The proof logic needed for verification is already in use by researchers in concurrent programming. The approach involves expressing the program in a language called Swarm, and its specifications as assertions over the Swarm program. Among models that employ rules-based notation, Swarm is the first to have an axiomatic proof logic. A brief review of Swarm and its proof logic is given, along with an illustration of the formal verification method used on a simple rule-based program

A counter abstraction technique for the verification of robot swarms.

We study parameterised verification of robot swarms against temporal-epistemic specifications. We relax some of the significant restrictions assumed in the literature and present a counter abstraction approach that enable us to verify a potentially much smaller abstract model when checking a formula on a swarm of any size. We present an implementation and discuss experimental results obtained for the alpha algorithm for robot swarms

Applying Formal Verification Methods to Pure Rule-Based Programs

Reliability, defined as the guarantee that a program satisfies its specifications, is an important aspect of many applications for which rule-based expert systems are suited. Verification refer to the process used to determine the reliability of the rule-based program. Because past approaches to verification are informal, guarantees of reliability cannot fully be made without severely restricting the system. On the other hand, by constructing formal specifications for a program and showing the program satisfies those specifications, guarantees of reliability can be made. This paper presents an assertional approach to the verification of rule-based programs. The proof logical needed for verification is adopted from one already in use by researchers in concurrent programming. The approach involves using a language called Swarm, and requires one to express program specifications as assertions over the Swarm representation of the program. Among models the employ rule-based notation, Swarm is the first to have an axiomatic proof logic

Verification of Localization via Blockchain Technology on Unmanned Aerial Vehicle Swarm

Verification of the geographic location of a moving device is vital. This verification is important in terms of ensuring that the flying systems moving in the swarm are in orbit and that they are able to task completion and manage their energy efficiency. Cyber-attacks on unmanned aerial vehicles (UAV) in a swarm can affect their position and cause various damages. In order to avoid this challenge, it is necessary to share with each other the positions of UAV in the swarm and to increase their accuracy. In this study, it is aimed to increase position accuracy and data integrity of UAV by using blockchain technology in swarm. Experiments were conducted on a virtual UAV network (UAVNet). Successful results were obtained from this proposed study

Overview on agent-based social modelling and the use of formal languages

Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft