Fuzzy argumentation for trust

In an open Multi-Agent System, the goals of agents acting on behalf of their owners often conflict with each other. Therefore, a personal agent protecting the interest of a single user cannot always rely on them. Consequently, such a personal agent needs to be able to reason about trusting (information or services provided by) other agents. Existing algorithms that perform such reasoning mainly focus on the immediate utility of a trusting decision, but do not provide an explanation of their actions to the user. This may hinder the acceptance of agent-based technologies in sensitive applications where users need to rely on their personal agents. Against this background, we propose a new approach to trust based on argumentation that aims to expose the rationale behind such trusting decisions. Our solution features a separation of opponent modeling and decision making. It uses possibilistic logic to model behavior of opponents, and we propose an extension of the argumentation framework by Amgoud and Prade to use the fuzzy rules within these models for well-supported decisions

Bounded Decentralised Coordination over Multiple Objectives

We propose the bounded multi-objective max-sum algorithm (B-MOMS), the first decentralised coordination algorithm for multi-objective optimisation problems. B-MOMS extends the max-sum message-passing algorithm for decentralised coordination to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Specifically, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Furthermore, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds 2, and is typically less than 1.5 for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds 30 minutes, even for maximally constrained graphs with $100$ agents. Thus, B-MOMS brings the problem of multi-objective optimisation well within the boundaries of the limited capabilities of embedded agents

Decentralised coordination of information gathering agents

Unmanned sensors are rapidly becoming the de facto means of achieving situational awareness — the ability to make sense of, and predict what is happening in an environment — in disaster management, military reconnaissance, space exploration, and climate research. In these domains, and many others besides, their use reduces the need for exposing humans to hostile, impassable or polluted environments. Whilst these sensors are currently often pre-programmed or remotely controlled by human operators, there is a clear trend toward making these sensors fully autonomous, thus enabling them to make decisions without human intervention.Full autonomy has two clear benefits over pre-programming and human remote control. First, in contrast to sensors with pre-programmed motion paths, autonomous sensors are better able to adapt to their environment, and react to a priori unknown external events or hardware failure. Second, autonomous sensors can operate in large teams that would otherwise be too complex to control by human operators. The key benefit of this is that a team of cheap, small sensors can achieve through cooperation the same results as individual large, expensive sensors — with more flexibility and robustness.In light of the importance of autonomy and cooperation, we adopt an agent-based perspective on the operation of the sensors. Within this view, each sensor becomes an information gathering agent. As a team, these agents can then direct their collective activity towards collecting information from their environment with the aim of providingaccurate and up-to-date situational awareness.Against this background, the central problem we address in this thesis is that of achieving accurate situational awareness through the coordination of multiple information gathering agents. To achieve general and principled solutions to this problem, we formulate a generic problem definition, which captures the essential properties of dynamic environments. Specific instantiations of this generic problem span a broad spectrum of concrete application domains, of which we study three canonical examples: monitoring environmental phenomena, wide area surveillance, and search and patrol.The main contributions of this thesis are decentralised coordination algorithms that solve this general problem with additional constraints and requirements, and can be grouped into two categories. The first category pertains to decentralised coordination of fixed information gathering agents. For these agents, we study the application of decentralised coordination during two distinct phases of the agents’ life cycle: deployment and operation. For the former, we develop an efficient algorithm for maximising the quality of situational awareness, while simultaneously constructing a reliable communication network between the agents. Specifically, we present a novel approach to the NP-hard problem of frequency allocation, which deactivates certain agents such that the problem can be provably solved in polynomial time. For the latter, we address the challenge of coordinating these agents under the additional assumption that their control parameters are continuous. In so doing, we develop two extensions to the max-sum message passing algorithm for decentralised welfare maximisation, which constitute the first two algorithms for distributed constraint optimisation problems (DCOPs) with continuous variables—CPLF-MS (for linear utility functions) and HCMS (for non-linear utility functions).The second category relates to decentralised coordination of mobile information gathering agents whose motion is constrained by their environment. For these agents, we develop algorithms with a receding planning horizon, and a non-myopic planning horizon. The former is based on the max-sum algorithm, thus ensuring an efficient and scalable solution, and constitutes the first online agent-based algorithm for the domains of pursuit-evasion, patrolling and monitoring environmental phenomena. The second uses sequential decision making techniques for the offline computation of patrols — infinitely long paths designed to continuously monitor a dynamic environment — which are subsequently improved on at runtime through decentralised coordination.For both topics, the algorithms are designed to satisfy our design requirements of quality of situational awareness, adaptiveness (the ability to respond to a priori unknown events), robustness (the ability to degrade gracefully), autonomy (the ability of agents to make decisions without the intervention of a centralised controller), modularity (the ability to support heterogeneous agents) and performance guarantees (the ability to give a lower bound on the quality of the achieved situational awareness). When taken together, the contributions presented in this thesis represent an advance in the state of the art of decentralised coordination of information gathering agents, and a step towards achieving autonomous control of unmanned sensors

Coordinating Teams of Mobile Sensors for Monitoring Environmental Phenomena

Mobile wireless sensors can play a vital role in achieving situational awareness in uncertain and changing environments by keeping track of environmental phenomena, such as temperature, gas concentration and radiation, that exhibit spatial and temporal correlations. Examples of such environments are commonly found in disaster response, where the safety and effectiveness of response units critically depends on the accuracy of estimation of the state of the world. In these environments, mobile sensors operating in a team can improve situational awareness by offering a high sensing resolution in a timely and efficient way. In order to do this efficiently, however, they need to coordinate their movements. This coordination is a challenging task, since the sensors operate in an environment that is highly uncertain and dynamic, have a limited perception of their surroundings, and have limited communication with adjacent sensors. Consequently, coordination mechanisms need to address the challenges involved in maximising the collective information gain of the entire team, in the presence of uncertainty and different world views. Previous work in this area has focused on the use mobile and fixed wireless sensors for environmental monitoring, but fails to provide a principled online, decentralised coordination mechanism for such settings. In this report, we study the challenge of coordinating teams of mobile sensors for monitoring environmental phenomena. In order to do so, we review the literature on wireless (mobile) sensor networks, information processing, target tracking, and localisation and mapping. In particular, we focus on the key concept of adaptive sampling, which encompasses a set of techniques that aim to maximise information gain subject to movement constraints and the limited resources at a sensor's disposal. Based on this review, we present a general architecture for a sensor that makes a clear distinction between information processing, information valuing and maximising information gain. In more detail, we show that the state of the art in adaptive sampling falls short of providing robust, scalable, decentralised coordination algorithms. To address these shortcomings, we develop two online, decentralised coordination algorithms for monitoring spatial phenomena. The first algorithm operates in an un-negotiated coordination mode, in which coordination is achieved exclusively through the exchange of observations; sensors need not coordinate (negotiate) about the actions they are about to take, but base their decisions solely on the picture of the state of the environment that they compiled using their own observations and those received from their neighbours. This algorithm is based on two techniques found in previous work. Firstly, Gaussian process regression (Rasmussen2006a, Osborne2008), which is used for processing the raw observations obtained by the sensors and for predicting unobserved measurements. Secondly, myopic information-theoretic control (as found in Grocholsky2002), which is used for maximising the informativeness of the samples that are obtained by moving the sensors to locations where the environment is more uncertain. The second algorithm extends the first by adding a negotiation stage, which results in negotiated coordination. This algorithm is based on the max-sum message passing algorithm for decentralised control (Farinelli2008), which allows the sensors to maximise a team objective function in a decentralised fashion. To make the max-sum algorithm suitable for solving the mobile sensor coordination problem, we develop two pruning algorithms that drastically reduce the amount of computation required. These pruning algorithms are generic in the context of applying the max-sum algorithm, and are thus not limited to the mobile sensor setting. Finally, we extend the negotiated algorithm for sensors that are characterised by continuous control parameters (for example their heading and velocity). To this end, we generalise the discrete max-sum algorithm to the continuous case in which the interactions between sensors are characterised by continuous piecewise linear functions

Decentralised Coordination of Mobile Sensors Using the Max-Sum Algorithm

In this paper, we introduce an on-line, decentralised coordination algorithm for monitoring and predicting the state of spatial phenomena by a team of mobile sensors. These sensors have their application domain in disaster response, where strict time constraints prohibit path planning in advance. The algorithm enables sensors to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations using a Gaussian process. It builds upon the max-sum message passing algorithm for decentralised coordination, for which we present two new generic pruning techniques that result in speed-up of up to 92% for 5 sensors. We empirically evaluate our algorithm against several on-line adaptive coordination mechanisms, and report a reduction in root mean squared error up to 50% compared to a greedy strategy

A Hybrid Continuous Max-Sum Algorithm for Decentralised Coordination

Recent advances in decentralised coordination of multiple agents have led to the proposal of the max-sum algorithm for solving distributed constraint optimisation problems (DCOPs). The max-sum algorithm is fully decentralised, converges to optimality for problems with acyclic constraint graphs and otherwise performs well in empirical studies. However, it requires agents to have discrete state spaces, which are of practical size to conduct repeated searches over. In contrast, there are decentralised non-linear optimisation methods that are capable of accurately finding local optima over multi-dimensional continuous state spaces, however these methods are not designed to navigate complex interactions between local constraints in order to find globally optimal solutions. Given this background, in this paper we tackle the problem of coordinating multiple decentralised agents with continuous state variables. Specifically we propose a hybrid approach, which combines the max-sum algorithm with continuous non-linear optimisation methods. We show that, for problems with acyclic factor graph representations, for suitable parameter choices, our proposed algorithm converges to a state with utility close to the global optimum. We empirically evaluate our approach for cyclic constraint graphs in a multi-sensor target classification problem, and compare its performance to the discrete max-sum algorithm, as well as a non-coordinated approach and the distributed stochastic algorithm (DSA). We show that our hybrid max-sum algorithm outperforms the non-coordinated algorithm, DSA and discrete max-sum considerably. Furthermore, the improvements in outcome over discrete max-sum come without significant increases in running time nor communication cost

Decentralised Control of Continuously Valued Control Parameters using the Max-Sum Algorithm

In this paper we address the problem of decentralised coordination for agents that must make coordinated decisions over continuously valued control parameters (as is required in many real world applications). In particular, we tackle the social welfare maximisation problem, and derive a novelcontinuous version of the max-sum algorithm. In order to do so, we represent the utility functionof the agents by multivariate piecewise linear functions, which in turn are encoded as simplexes.We then derive analytical solutions for the fundamental operations required to implement the max-sum algorithm (specifically, addition and marginal maximisation of general n-ary piecewise linearfunctions). We empirically evaluate our approach on a simulated network of wireless, energy constrained sensors that must coordinate their sense/sleep cycles in order to maximise the system-wide probability of event detection. We compare the conventional discrete max-sum algorithm with our novel continuous version, and show that the continuous approach obtains more accurate solutions (up to a 10% increase) with a lower communication overhead (up to half of the total message size)

A Distributed Anytime Algorithm for Dynamic Task Allocation in Multi-Agent Systems

We introduce a novel distributed algorithm for multi-agent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fast-max-sum), and give it significant new capa- bilities: namely, an online pruning procedure that simplifies the problem, and a branch-and-bound technique that reduces the search space. This allows us to scale to problems with hundreds of tasks and agents. We empirically evaluate our algorithm against established benchmarks and find that, even in such large environments, a solution is found up to 31% faster, and with up to 23% more utility, than state-of-the-art approximation algorithms. In addition, our algorithm sends up to 30% fewer messages than current approaches when the set of agents or tasks changes