Dynamic decision networks for decision-making in self-adaptive systems:a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and speci?cally in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, speci?cally Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential bene?ts of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision.

The multiple pheromone ant clustering algorithm and its application to real world domains

The Multiple Pheromone Ant Clustering Algorithm (MPACA) models the collective behaviour of ants to find clusters in data and to assign objects to the most appropriate class. It is an ant colony optimisation approach that uses pheromones to mark paths linking objects that are similar and potentially members of the same cluster or class. Its novelty is in the way it uses separate pheromones for each descriptive attribute of the object rather than a single pheromone representing the whole object. Ants that encounter other ants frequently enough can combine the attribute values they are detecting, which enables the MPACA to learn influential variable interactions. This paper applies the model to real-world data from two domains. One is logistics, focusing on resource allocation rather than the more traditional vehicle-routing problem. The other is mental-health risk assessment. The task for the MPACA in each domain was to predict class membership where the classes for the logistics domain were the levels of demand on haulage company resources and the mental-health classes were levels of suicide risk. Results on these noisy real-world data were promising, demonstrating the ability of the MPACA to find patterns in the data with accuracy comparable to more traditional linear regression models. © 2013 Polish Information Processing Society.

A multiple pheromone ant clustering algorithm

Ant colony optimisation algorithms model the way ants use pheromones for marking paths to important locations in their environment. Pheromone traces are picked up, followed, and reinforced by other ants but also evaporate over time. Optimal paths attract more pheromone and less useful paths fade away. The main innovation of the proposed Multiple Pheromone Ant Clustering Algorithm (MPACA) is to mark objects using many pheromones, one for each value of each attribute describing the objects in multidimensional space. Every object has one or more ants assigned to each attribute value and the ants then try to find other objects with matching values, depositing pheromone traces that link them. Encounters between ants are used to determine when ants should combine their features to look for conjunctions and whether they should belong to the same colony. This paper explains the algorithm and explores its potential effectiveness for cluster analysis. © 2014 Springer International Publishing Switzerland.

The multiple pheromone Ant clustering algorithm

Ant Colony Optimisation algorithms mimic the way ants use pheromones for marking paths to important locations. Pheromone traces are followed and reinforced by other ants, but also evaporate over time. As a consequence, optimal paths attract more pheromone, whilst the less useful paths fade away. In the Multiple Pheromone Ant Clustering Algorithm (MPACA), ants detect features of objects represented as nodes within graph space. Each node has one or more ants assigned to each feature. Ants attempt to locate nodes with matching feature values, depositing pheromone traces on the way. This use of multiple pheromone values is a key innovation. Ants record other ant encounters, keeping a record of the features and colony membership of ants. The recorded values determine when ants should combine their features to look for conjunctions and whether they should merge into colonies. This ability to detect and deposit pheromone representative of feature combinations, and the resulting colony formation, renders the algorithm a powerful clustering tool. The MPACA operates as follows: (i) initially each node has ants assigned to each feature; (ii) ants roam the graph space searching for nodes with matching features; (iii) when departing matching nodes, ants deposit pheromones to inform other ants that the path goes to a node with the associated feature values; (iv) ant feature encounters are counted each time an ant arrives at a node; (v) if the feature encounters exceed a threshold value, feature combination occurs; (vi) a similar mechanism is used for colony merging. The model varies from traditional ACO in that: (i) a modified pheromone-driven movement mechanism is used; (ii) ants learn feature combinations and deposit multiple pheromone scents accordingly; (iii) ants merge into colonies, the basis of cluster formation. The MPACA is evaluated over synthetic and real-world datasets and its performance compares favourably with alternative approaches.

Efficient Learning of Bayesian Networks with Bounded Tree-width

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods \cite{korhonen2exact, nie2014advances} tackle the problem by using $k$-trees to learn the optimal Bayesian network with tree-width up to $k$. Finding the best $k$-tree, however, is computationally intractable. In this paper, we propose a sampling method to efficiently find representative $k$-trees by introducing an informative score function to characterize the quality of a $k$-tree. To further improve the quality of the $k$-trees, we propose a probabilistic hill climbing approach that locally refines the sampled $k$-trees. The proposed algorithm can efficiently learn a quality Bayesian network with tree-width at most $k$. Experimental results demonstrate that our approach is more computationally efficient than the exact methods with comparable accuracy, and outperforms most existing approximate methods.

Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables

We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian network greatly reduces the complexity of inferences. Yet, being a global property of the graph, it considerably increases the difficulty of the learning process. Our novel algorithm accomplishes this task, scaling both to large domains and to large treewidths. Our novel approach consistently outperforms the state of the art on experiments with up to thousands of variables.

Multi-objective ant colony optimization for requirements selection

The selection of a set of requirements between all the requirements previously defined by customers is an important process, repeated at the beginning of each development step when an incremental or agile software development approach is adopted. The set of selected requirements will be developed during the actual iteration. This selection problem can be reformulated as a search problem, allowing its treatment with metaheuristic optimization techniques. This paper studies how to apply Ant Colony Optimization algorithms to select requirements. First, we describe this problem formally extending an earlier version of the problem, and introduce a method based on Ant Colony System to find a variety of efficient solutions. The performance achieved by the Ant Colony System is compared with that of Greedy Randomized Adaptive Search Procedure and Non-dominated Sorting Genetic Algorithm, by means of computational experiments carried out on two instances of the problem constructed from data provided by the experts.

A rank-based ant system algorithm for solving 0/1 knapsack problem

The 0/1 Knapsack Problem (KP), which is a classical NP-complete problem, has been widely applied to solving many real world problems. Ant system (AS), as one of the earliest ant colony optimization (ACO) algorithms, provides approximate solutions to 0/1 KPs. However, there are some shortcomings such as low efficiency and premature convergence in most AS algorithms. In order to overcome the shortcomings of AS, this paper proposes a rank-based AS algorithm, denoted as RAS to solve 0/1 KP. Taking advantages of the ranked ants with a higher profit, the pheromone of items will be updated with better solutions in RAS. Experimental results in different datasets show that this new kind of AS algorithm can obtain a higher efficiency and robustness when solving 0/1 KP.

Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.

Urban maze : A typological investigation in porous morphology and dynamic pedestrian networks

Mixed use typologies and pedestrian networks are two strategies commonly applied in design of the contemporary city. These approaches, aimed towards the creation of a more sustainalble urban environment, have their roots in the traditional, pre-industrial towns; they characterize urban form, articulating the tension between privaate and public realms through a series of typological variations as well as stimulating commercial activity in the city centre. Arcades, loggias and verandas are just some of the elements which can mediate this tension. Historically they have defined physical and social spaces with particular character; in the contemporary city these features are applied to deform the urban form and create a porous, dynamic morphology. This paper, comparing case studies from Italy, Japan and Australia, investigates how the design of the transition zone can define hybrid pedestrian networks, where a clear distinction between the public and private realms is no longer applicable. Pedestrians use the city in a dynamic way, combining trajectories on the public street with ones on the fringe or inside of the private built environment. In some cases, cities offer different pedestrian network possibilities at different times, as the commercial precints are subject to variations in accessibility across various timeframes. These walkable systems have an impact on the urban form and identity of places, redefining typologies and requiring an in depth analysis through plan, section and elevation diagrams.

Expert elicitation into Bayesian networks : understanding probability

An introduction to thinking about and understanding probability that highlights the main pits and trapfalls that befall logical reasoning

Expert elicitation into Bayesian networks, Part 1 of 4 : understanding probability

An introduction to elicitation of experts' probabilities, which illustrates common problems with reasoning and how to circumvent them during elicitation.

Expert elicitation into Bayesian networks, part 2 of 4: designing elicitation

An introduction to design of eliciting knowledge from experts.