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In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the above-mentioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO. Note to Practitioners —This paper was motivated by the limitation of current distributed task allocation algorithms as they cannot achieve performances that are as good as the centralized ones. Therefore, a centralized algorithm is designed to evaluate the performance gap between the state-of-the-art distributed and centralized approaches. In the future research section, a new distributed particle swarm optimization (PSO) algorithm is proposed based on this paper as the research has shown that the proposed centralized PSO algorithm delivers the best results so it is potentially a strong candidate for adaptation.

The aim of this work is to produce and test a robustness module (ROB-M) that can be generally applied to distributed, multi-agent task allocation algorithms, as robust versions of these are scarce and not well-documented in the literature. ROB-M is developed using the Performance Impact (PI) algorithm, as this has previously shown good results in deterministic trials. Different candidate versions of the module are thus bolted on to the PI algorithm and tested using two different task allocation problems under simulated uncertain conditions, and results are compared with baseline PI. It is shown that the baseline does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. However, when PI is run with one of the candidate robustness modules, the failure rate becomes very low for both problems, even under high simulated uncertainty, and so its architecture is adopted for ROB-M and also applied to MIT’s baseline Consensus Based Bundle Algorithm (CBBA) to demonstrate its flexibility. Strong evidence is provided to show that ROB-M can work effectively with CBBA to improve performance under simulated uncertain conditions, as long as the deterministic versions of the problems can be solved with baseline CBBA. Furthermore, the use of ROB-M does not appear to increase mean task completion time in either algorithm, and only 100 Monte Carlo samples are required compared to 10,000 in MIT’s robust version of the CBBA algorithm. PI with ROB-M is also tested directly against MIT’s robust algorithm and demonstrates clear superiority in terms of mean numbers of solved tasks.

This paper addresses two main problems with many heuristic task allocation approaches—solution trapping in local minima and static structure. The existing distributed task allocation algorithm known as performance impact (PI) is used as the vehicle for developing solutions to these problems as it has been shown to outperform the state-of-the-art consensus-based bundle algorithm for time-critical problems with tight deadlines, but is both static and suboptimal with a tendency toward trapping in local minima. This paper describes two additional modules that are easily integrated with PI. The first extends the algorithm to permit dynamic online rescheduling in real time, and the second boosts performance by introducing an additional soft-max action-selection procedure that increases the algorithm’s exploratory properties. This paper demonstrates the effectiveness of the dynamic rescheduling module and shows that the average time taken to perform tasks can be reduced by up to 9% when the soft-max module is used. In addition, the solution of some problems that baseline PI cannot handle is enabled by the second module. These developments represent a significant advance in the state of the art for multiagent, time-critical task assignment. Note to Practitioners —This work was motivated by the limitations of current agent-to-task allocation algorithms that do not use a central server for communication. In previously published work, the current state-of-the-art consensus-based bundle algorithm has demonstrated poor performance when applied to model task allocation problems with critical time limits, often failing to assign all of the tasks, especially when the deadlines are tight. The performance impact (PI) algorithm has a much better success rate with these model problems but would be flawed when applied to real missions because it has no mechanism for online replanning when new information becomes available. In addition, it is somewhat restricted in the way it searches for a problem solution, meaning that more efficient plans are often available but are not discovered. This paper tackles both of these shortcomings. The PI algorithm is extended to include a module that permits rescheduling when necessary, and a further module is introduced that widens the scope of the solution search. A third module that is able to offer robust plans, even for large-scaled missions involving many agents and tasks, has also been developed, although it is not discussed here. Implementation and testing of a version of PI that incorporates all three of these modules are the final goal of this research.

Maintaining safety in roundabouts is crucial in autonomous vehicles (AV) controlling and path planning. The number of vehicles in a roundabout at a time and the rules they must obey can make it a very complex traffic environment. Before an AV starts entering a roundabout, oncoming vehicles must be identified, and it could be done by determining their position, speed and direction. To address that, this paper extends some of our previous works in AV decision making in roundabouts and proposes a multi-grid-based image processing approach using multiple cameras (MGC). Particularly, it utilises a fine grid to determine speed and direction of approaching vehicles, whilst the position is evaluated using a larger grid. Besides, using multiple cameras allows the system to mimic the real drivers’ view and perception in approaching the real roundabouts, hence a human- like decision can be made. Three different classifiers including SVM, ANN and kNN were examined using 460 video clips of real roundabout-drive circumstances. The highest score was obtained by SVM at nearly 97% accuracy rate, with the making decision time is only around one second. That promising result indicates the applicability of the MGC system in real traffic situations.

Being able to join roundabouts correctly is crucial for an autonomous vehicle to maintain not only its own safety but also a normal traffic order for others. In order to know the right time and speed for entering roundabouts, the location, speed and direction of the approaching vehicles need to be taken into consideration. This study investigated the feasibility of leveraging computer vision and machine learning to help autonomous vehicles decide to wait or to enter when reaching roundabouts. A grid-based image processing approach with a single camera at normal roundabouts (GBIPA-SC-NR) is proposed in this paper to characterize traffic situations that can be used for machine learning algorithms to learn the roundabout joining criteria. Video road clips recorded when human drivers reach and then join various roundabouts at different locations were utilised for this learning process, with a selection of four supervised classification algorithms (i.e. the Support Vector Machines, Random Forests, K-Nearest Neighbours, and Decision Tree). The trained classifiers using the proposed approach were evaluated on 507 test videos captured at roundabouts, where the SVM showed the best performance with a 90.28% classification accuracy. This result suggests that the proposed grid-based image processing method can be applied to effectively help autonomous vehicles made the right decision when reaching a roundabout.

Cerebral Visual Impairment (CVI) is a medical area that concerns the study of the effect of brain damages on the visual field (VF). People with CVI have difficulties in their mobility and they have behaviours that others find hard to understand due to their visual impairment. A branch of Artificial Intelligence (AI) is the simulation of behaviour by building computational models that help to explain how people solve problems or why they behave in a certain way. This paper describes a novel computational system that simulates the navigation problem that is faced by people with CVI. This will help relatives, friends, and ophthalmologists of CVI patients understand more about their difficulties in navigating their everyday environment.

The aim of this work is to produce and test a robust, distributed, multi-agent task allocation algorithm, as these are scarce and not well-documented in the literature. The vehicle used to create the robust system is the Performance Impact algorithm (PI), as it has previously shown good performance. Three different variants of PI are designed to improve its robustness, each using Monte Carlo sampling to approximate Gaussian distributions. Variant A uses the expected value of the task completion times, variant B uses the worst-case scenario metric and variant C is a hybrid that implements a combination of these. The paper shows that, in simulated trials, baseline PI does not handle uncertainty well; the task-allocation success rate tends to decrease linearly as degree of uncertainty increases. Variant B demonstrates a worse performance and variant A improves the failure rate only slightly. However, in comparison, the hybrid variant C exhibits a very low failure rate, even under high uncertainty. Furthermore, it demonstrates a significantly better mean objective function value than the baseline.

Considerable current research is focusing on the subject of "Wellbeing and aging", which covers a wide range of solutions to help encourage elderly people to engage on routine physical exercise. In particular, elderly communities in nursing homes are usually involved in activities on rehabilitation, daily exercises and health tracking. In order to implement an automated system for elderly exercise monitoring, human motion analysis should be efficiently performed in an affordable way, and delivered in a way that the users understand. In this paper, a general framework for comparison of fitness performances in the context of basic stand-up physical activities is presented. The Microsoft Kinect device is used for motion capture. A method for key body pose prediction on human activities based on multi-class C4.5, SVM, Naive Bayes and AdaBoost classifiers is first introduced, followed by a cluster analysis to refine the obtained results. The performances achieved with the different techniques and parameters are then analyzed and the best configuration compares favorably against the DTW and HACA algorithms.

Lesson planning imposes a significant burden on teachers as they need to prepare different lesson plans for different classes according to various constraints. SmartLP, a case-based lesson planning system, has been implemented as a means of assisting teachers in constructing quality lesson plans more quickly. SmartLP enables teachers to retrieve previous lesson plans and customise them according to their constraints as they should be tailored to accommodate differences in students, teachers and the facilities available. An evaluation study was designed to examine the effectiveness of a Case Based Reasoning (CBR) approach through its cycle of retrieve, reuse, revise and retain in assisting teachers in constructing lesson plans and to assess the effects, in terms of time and quality, the system had on lesson plan preparation. This paper presents the results of the study. The study revealed that all the lesson plans constructed with SmartLP took significantly less time than lesson plans constructed without. In addition, where the planning tasks had greater similarities to the cases in the case base, they took even less time to be constructed. No significant difference in the quality of the plans, measured by a scoring system, was noticed between the control and experimental groups.

Workflow technologies are widely used in industry and commerce to assist in the specification, execution and completion of well defined processes within organisations. As industrial and commercial relations have evolved, based on advances on information and communications technologies, cross-organisational workflow integration has become an important issue. Since organisations can have very different workflows, the creation of compatible workflows so that organisations can collaborate and/or carry out mutual transactions automatically in an integrated fashion can be a very complex and time consuming process. As a consequence, the development of technologies to support the creation and execution of compatible workflows is a most relevant issue. In the present article we introduce the JamSession coordination platform as a tool to implement cross-organisational workflow integration. JamSession is declarative and based on algebraic specification methods, and therefore workflow integration implemented using this platform can profit from formal behavioural analysis, based on which desired features and properties can be verified and/or obtained.

The consideration of safety of processing plants from design to operation involves many tasks. Different techniques and tools have been developed to assist in carry out those tasks. With the advances in computing technologies, both in hardware and software, there is the opportunity to bring together the vast amount of engineering, hazard and risk data and handle them in an efficient way to manage and improve safety throughout the life time of a plant. This paper proposes keeping such data in a process plant safety information repository which can be easily accessed by different safety-related tools to enhance the efficiency of safety identification and analysis tasks.