Combining Exception Handling and Replication for Improving the Reliability of Agent Software

Abstract. Exception handling and replication are two complementary mechanisms that increase software reliability. Exception handling helps programmers in controlling situations in which the normal execution flow of a program cannot continue. Replication handles system failures through redundancy. Combining both techniques is a first step towards building a trustworthy software engineering framework. This paper presents some of the results from the Facoma project. It proposes the specification of an exception handling system for replicated agents as an adaptation of the Sage proposal. It then describes its implementation in the Dimax replicated agent environment

A Dynamic, Modular Intelligent-Agent framework for Astronomical Light Curve Analysis and Classification

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. This makes it almost impossible for objects to be identified manually. Therefore the production of methods and systems for the automated classification of time-domain astro-nomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure. These in-struments have been in operation since March 2009 gathering data of multi-degree sized areas of sky around the current field of view of the main telescope. Utilizing a Structured Query Language database established by a pre-processing operation upon the resultant images, which has identified millions of candidate variable stars with multiple time-varying magnitude observations, we applied a method designed to extract time-translation invariant features from the time-series light curves of each object for future input into a classification system. These efforts were met with limited success due to noise and uneven sampling within the time-series data. Additionally, finely surveying these light curves is a processing intensive task. Fortunately, these algorithms are capable of multi-threaded implementations based on available resources. Therefore we propose a new system designed to utilize multiple intelligent agents that distribute the data analysis across multiple machines whilst simultaneously a powerful intelligence service operates to constrain the light curves and eliminate false signals due to noise and local alias periods. This system will be highly scalable, capable of operating on a wide range of hardware whilst maintaining the production of ac-curate features based on the fitting of harmonic models to the light curves within the initial Structural Query Language database

Exploiting the Use of Cooperation in Self-Organizing Reliable Multiagent Systems

In this paper, a novel and cooperative approach is exploited introducing a self-organizing engine to achieve high reliability and availability in multiagent systems. The Adaptive Multiagent Systems theory is applied to design adaptive groups of agents in order to build reliable multiagent systems. According to this theory, adaptiveness is achieved via the cooperative behaviors of agents and their ability to change the communication links autonomously. In this approach, there is not a centralized control mechanism in the multiagent system and there is no need of global knowledge of the system to achieve reliability. This approach was implemented to demonstrate its performance gain in a set of experiments performed under different operating conditions. The experimental results illustrate the effectiveness of this approach

DNAgents: Genetically Engineered Intelligent Mobile Agents

Mobile agents are a useful paradigm for network coding providing many advantages and disadvantages. Unfortunately, widespread adoption of mobile agents has been hampered by the disadvantages, which could be said to outweigh the advantages. There is a variety of ongoing work to address these issues, and this is discussed. Ultimately, genetic algorithms are selected as the most interesting potential avenue. Genetic algorithms have many potential benefits for mobile agents. The primary benefit is the potential for agents to become even more adaptive to situational changes in the environment and/or emergent security risks. There are secondary benefits such as the natural obfuscation of functions inherent to genetic algorithms. Pitfalls also exist, namely the difficulty of defining a satisfactory fitness function and the variable execution time of mobile agents arising from the fact that it exists on a network. DNAgents 1.0, an original application of genetic algorithms to mobile agents is implemented and discussed, and serves to highlight these difficulties. Modifications of traditional genetic algorithms are also discussed. Ultimately, a combination of genetic algorithms and artificial life is considered to be the most appropriate approach to mobile agents. This allows the consideration of agents to be organisms, and the network to be their environment. Towards this end, a novel framework called DNAgents 2.0 is designed and implemented. This framework allows the continual evolution of agents in a network without having a seperate training and deployment phase. Parameters for this new framework were defined and explored. Lastly, an experiment similar to DNAgents 1.0 is performed for comparative purposes against DNAgents 1.0 and to prove the viability of this new framework

Locality-driven checkpoint and recovery

Checkpoint and recovery are important fault-tolerance techniques for distributed systems. The two categories of existing strategies incur unacceptable performance cost either at run time or upon failure recovery, when applied to large-scale distributed systems. In particular, the large number of messages and processes in these systems causes either considerable checkpoint as well as logging overhead, or catastrophic global-wise recovery effect. This thesis proposes a locality-driven strategy for efficiently checkpointing and recovering such systems with both affordable runtime cost and controllable failure recoverability. Messages establish dependencies between distributed processes, which can be either preserved by coordinated checkpoints or removed via logging. Existing strategies enforce a uniform handling policy for all message dependencies, and hence gains advantage at one end but bears disadvantage at the other. In this thesis, a generic theory of Quasi-Atomic Recovery has been formulated to accommodate message handling requirements of both kinds, and to allow using different message handling methods together. Quasi-atomicity of recovery blocks implies proper confinement of recoveries, and thus enables localization of checkpointing and recovery around such a block and consequently a hybrid strategy with combined advantages from both ends. A strategy of group checkpointing with selective logging has been proposed, based on the observation of message localization around 'locality regions' in distributed systems. In essence, a group-wise coordinated checkpoint is created around such a region and only the few inter-region messages are logged subsequently. Runtime overhead is optimized due to largely reduced logging efforts, and recovery spread is as localized as region-wise. Various protocols have been developed to provide trade-offs between flexibility and performance. Also proposed is the idea of process clone that can be used to effectively remove program-order recovery dependencies among successive group checkpoints and thus to stop inter-group recovery spread. Distributed executions exhibit locality of message interactions. Such locality originates from resolving distributed dependency localization via message passing, and appears as a hierarchical 'region-transition' pattern. A bottom-up approach has been proposed to identify those regions, by detecting popular recurrence patterns from individual processes as 'locality intervals', and then composing them into 'locality regions' based on their tight message coupling relations between each other. Experiments conducted on real-life applications have shown the existence of hierarchical locality regions and have justified the feasibility of this approach. Performance optimization of group checkpoint strategies has to do with their uses of locality. An abstract performance measure has been-proposed to properly integrate both runtime overhead and failure recoverability in a region-wise marner. Taking this measure as the optimization objective, a greedy heuristic has been introduced to decompose a given distributed execution into optimized regions. Analysis implies that an execution pattern with good locality leads to good optimized performance, and the locality pattern itself can serve as a good candidate for the optimal decomposition. Consequently, checkpoint protocols have been developed to efficiently identify optimized regions in such an execution, with assistance of either design-time or runtime knowledge

An agent-based visualisation system.

This thesis explores the concepts of visual supercomputing, where complex distributed systems are used toward interactive visualisation of large datasets. Such complex systems inherently trigger management and optimisation problems; in recent years the concepts of autonomic computing have arisen to address those issues. Distributed visualisation systems are a very challenging area to apply autonomic computing ideas as such systems are both latency and compute sensitive, while most autonomic computing implementations usually concentrate on one or the other but not both concurrently. A major contribution of this thesis is to provide a case study demonstrating the application of autonomic computing concepts to a computation intensive, real-time distributed visualisation system. The first part of the thesis proposes the realisation of a layered multi-agent system to enable autonomic visualisation. The implementation of a generic multi-agent system providing reflective features is described. This architecture is then used to create a flexible distributed graphic pipeline, oriented toward real-time visualisation of volume datasets. Performance evaluation of the pipeline is presented. The second part of the thesis explores the reflective nature of the system and presents high level architectures based on software agents, or visualisation strategies, that take advantage of the flexibility of the system to provide generic features. Autonomic capabilities are presented, with fault recovery and automatic resource configuration. Performance evaluation, simulation and prediction of the system are presented, exploring different use cases and optimisation scenarios. A performance exploration tool, Delphe, is described, which uses real-time data of the system to let users explore its performance

3D-in-2D Displays for ATC.

This paper reports on the efforts and accomplishments of the 3D-in-2D Displays for ATC project at the end of Year 1. We describe the invention of 10 novel 3D/2D visualisations that were mostly implemented in the Augmented Reality ARToolkit. These prototype implementations of visualisation and interaction elements can be viewed on the accompanying video. We have identified six candidate design concepts which we will further research and develop. These designs correspond with the early feasibility studies stage of maturity as defined by the NASA Technology Readiness Level framework. We developed the Combination Display Framework from a review of the literature, and used it for analysing display designs in terms of display technique used and how they are combined. The insights we gained from this framework then guided our inventions and the human-centered innovation process we use to iteratively invent. Our designs are based on an understanding of user work practices. We also developed a simple ATC simulator that we used for rapid experimentation and evaluation of design ideas. We expect that if this project continues, the effort in Year 2 and 3 will be focus on maturing the concepts and employment in a operational laboratory settings

CAMP-BDI: an approach for multiagent systems robustness through capability-aware agents maintaining plans

Rational agent behaviour is frequently achieved through the use of plans, particularly within the widely used BDI (Belief-Desire-Intention) model for intelligent agents. As a consequence, preventing or handling failure of planned activity is a vital component in building robust multiagent systems; this is especially true in realistic environments, where unpredictable exogenous change during plan execution may threaten intended activities. Although reactive approaches can be employed to respond to activity failure through replanning or plan-repair, failure may have debilitative effects that act to stymie recovery and, potentially, hinder subsequent activity. A further factor is that BDI agents typically employ deterministic world and plan models, as probabilistic planning methods are typical intractable in realistically complex environments. However, deterministic operator preconditions may fail to represent world states which increase the risk of activity failure. The primary contribution of this thesis is the algorithmic design of the CAMP-BDI (Capability Aware, Maintaining Plans) approach; a modification of the BDI reasoning cycle which provides agents with beliefs and introspective reasoning to anticipate increased risk of failure and pro-actively modify intended plans in response. We define a capability meta-knowledge model, providing information to identify and address threats to activity success using precondition modelling and quantitative quality estimation. This also facilitates semantic-independent communication of capability information for general advertisement and of dependency information - we define use of the latter, within a structured messaging approach, to extend local agent algorithms towards decentralized, distributed robustness. Finally, we define a policy based approach for dynamic modification of maintenance behaviour, allowing response to observations made during runtime and with potential to improve re-usability of agents in alternate environments. An implementation of CAMP-BDI is compared against an equivalent reactive system through experimentation in multiple perturbation configurations, using a logistics domain. Our empirical evaluation indicates CAMP-BDI has significant benefit if activity failure carries a strong risk of debilitative consequence