11,147 research outputs found
Collaborative signal and information processing for target detection with heterogeneous sensor networks
In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield
Neuroethology, Computational
Over the past decade, a number of neural network researchers have used the term computational neuroethology to describe a specific approach to neuroethology. Neuroethology is the study of the neural mechanisms underlying the generation of behavior in animals, and hence it lies at the intersection of neuroscience (the study of nervous systems) and ethology (the study of animal behavior); for an introduction to neuroethology, see Simmons and Young (1999). The definition of computational neuroethology is very similar, but is not quite so dependent on studying animals: animals just happen to be biological autonomous agents. But there are also non-biological autonomous agents such as some types of robots, and some types of simulated embodied agents operating in virtual worlds. In this context, autonomous agents are self-governing entities capable of operating (i.e., coordinating perception and action) for extended periods of time in environments that are complex, uncertain, and dynamic. Thus, computational neuroethology can be characterised as the attempt to analyze the computational principles underlying the generation of behavior in animals and in artificial autonomous agents
On the Simulation of Global Reputation Systems
Reputation systems evolve as a mechanism to build trust in virtual communities. In this paper we evaluate different metrics for computing reputation in multi-agent systems. We present a formal model for describing metrics in reputation systems and show how different well-known global reputation metrics are expressed by it. Based on the model a generic simulation framework for reputation metrics was implemented. We used our simulation framework to compare different global reputation systems to find their strengths and weaknesses. The strength of a metric is measured by its resistance against different threat-models, i.e. different types of hostile agents. Based on our results we propose a new metric for reputation systems.Reputation System, Trust, Formalization, Simulation
Cost-Bounded Active Classification Using Partially Observable Markov Decision Processes
Active classification, i.e., the sequential decision-making process aimed at
data acquisition for classification purposes, arises naturally in many
applications, including medical diagnosis, intrusion detection, and object
tracking. In this work, we study the problem of actively classifying dynamical
systems with a finite set of Markov decision process (MDP) models. We are
interested in finding strategies that actively interact with the dynamical
system, and observe its reactions so that the true model is determined
efficiently with high confidence. To this end, we present a decision-theoretic
framework based on partially observable Markov decision processes (POMDPs). The
proposed framework relies on assigning a classification belief (a probability
distribution) to each candidate MDP model. Given an initial belief, some
misclassification probabilities, a cost bound, and a finite time horizon, we
design POMDP strategies leading to classification decisions. We present two
different approaches to find such strategies. The first approach computes the
optimal strategy "exactly" using value iteration. To overcome the computational
complexity of finding exact solutions, the second approach is based on adaptive
sampling to approximate the optimal probability of reaching a classification
decision. We illustrate the proposed methodology using two examples from
medical diagnosis and intruder detection
Synthesized cooperative strategies for intelligent multi-robots in a real-time distributed environment : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Albany, New Zealand
In the robot soccer domain, real-time response usually curtails the development of more complex Al-based game strategies, path-planning and team cooperation between intelligent agents. In light of this problem, distributing computationally intensive algorithms between several machines to control, coordinate and dynamically assign roles to a team of robots, and allowing them to communicate via a network gives rise to real-time cooperation in a multi-robotic team. This research presents a myriad of algorithms tested on a distributed system platform that allows for cooperating multi- agents in a dynamic environment. The test bed is an extension of a popular robot simulation system in the public domain developed at Carnegie Mellon University, known as TeamBots. A low-level real-time network game protocol using TCP/IP and UDP were incorporated to allow for a conglomeration of multi-agent to communicate and work cohesively as a team. Intelligent agents were defined to take on roles such as game coach agent, vision agent, and soccer player agents. Further, team cooperation is demonstrated by integrating a real-time fuzzy logic-based ball-passing algorithm and a fuzzy logic algorithm for path planning. Keywords Artificial Intelligence, Ball Passing, the coaching system, Collaborative, Distributed Multi-Agent, Fuzzy Logic, Role Assignmen
Intelligent Agents for Disaster Management
ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains
Trustee: A Trust Management System for Fog-enabled Cyber Physical Systems
In this paper, we propose a lightweight trust management system (TMS) for fog-enabled cyber physical systems (Fog-CPS). Trust computation is based on multi-factor and multi-dimensional parameters, and formulated as a statistical regression problem which is solved by employing random forest regression model. Additionally, as the Fog-CPS systems could be deployed in open and unprotected environments, the CPS devices and fog nodes are vulnerable to numerous attacks namely, collusion, self-promotion, badmouthing, ballot-stuffing, and opportunistic service. The compromised entities can impact the accuracy of trust computation model by increasing/decreasing the trust of other nodes. These challenges are addressed by designing a generic trust credibility model which can countermeasures the compromise of both CPS devices and fog nodes. The credibility of each newly computed trust value is evaluated and subsequently adjusted by correlating it with a standard deviation threshold. The standard deviation is quantified by computing the trust in two configurations of hostile environments and subsequently comparing it with the trust value in a legitimate/normal environment. Our results demonstrate that credibility model successfully countermeasures the malicious behaviour of all Fog-CPS entities i.e. CPS devices and fog nodes. The multi-factor trust assessment and credibility evaluation enable accurate and precise trust computation and guarantee a dependable Fog-CPS system
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