Translucent fiber-optic networks are carefully planned to achieve high capacity utilization efficiency as required by society's ever-increasing traffic demand. Existing research treats the problem of resource plac...
详细信息
ISBN:
(纸本)9781467309202
Translucent fiber-optic networks are carefully planned to achieve high capacity utilization efficiency as required by society's ever-increasing traffic demand. Existing research treats the problem of resource placement largely as a static design problem, which is solved with linear programming (LP) to find the optimal solution. The dynamic operational problem (grooming, regeneration, routing, and wavelength assignment) is approached using heuristic methods with the goal of improving the overall network performance given an existing network infrastructure. Our work combines these two approaches and solves a real-time dynamic traffic scenario with integer linear programming (ILP), seeking to maximize the overall network throughput. The traffic is served in a time-slotted fashion so that the network throughput is optimized at each time slot given the existing network state. The solution is compared with results from existing heuristic methods. We incorporate physical impairment limitations into our network model, and consider several grooming options.
Normative frameworks provide a means to address the governance of open systems, offering a mechanism to express responsibilities and permissions of the individual participants with respect to the entire system without...
详细信息
ISBN:
(纸本)9783642378898
Normative frameworks provide a means to address the governance of open systems, offering a mechanism to express responsibilities and permissions of the individual participants with respect to the entire system without compromising their autonomy. In order to meet requirements careful design is crucial. Tools that support the design process can be of great benefit. In this paper, we describe and illustrate a methodology for elaborating normative specifications. We utilise use-cases to capture desirable and undesirable system behaviours, employ inductive logic programming to construct elaborations, in terms of revisions and extensions, of an existing (partial) normative specification and provide justifications as to why certain changes are better than others. The latter can be seen as a form of impact analysis of the possible elaborations, in terms of critical consequences that would be preserved or rejected by the changes. The main contributions of this paper is a (semi) automated process for controlling the elaboration of normative specifications and a demonstration of its effectiveness through a proof-of-concept case study.
In this paper we propose a use-case-driven iterative design methodology for normative frameworks, also called virtual institutions, which are used to govern open systems. Our computational model represents the normati...
详细信息
In this paper we propose a use-case-driven iterative design methodology for normative frameworks, also called virtual institutions, which are used to govern open systems. Our computational model represents the normative framework as a logic program under answer set semantics (ASP). By means of an inductive logic programming approach, implemented using ASP, it is possible to synthesise new rules and revise the existing ones. The learning mechanism is guided by the designer who describes the desired properties of the framework through use cases, comprising (i) event traces that capture possible scenarios, and (ii) a state that describes the desired outcome. The learning process then proposes additional rules, or changes to current rules, to satisfy the constraints expressed in the use cases. Thus, the contribution of this paper is a process for the elaboration and revision of a normative framework by means of a semi-automatic and iterative process driven from specifications of (un)desirable behaviour. The process integrates a novel and general methodology for theory revision based on ASP.
Reports of experiments conducted with an inductive logic programming system rarely describe how specific values of parameters of the system are arrived at when constructing models. Usually, no attempt is made to ident...
详细信息
Reports of experiments conducted with an inductive logic programming system rarely describe how specific values of parameters of the system are arrived at when constructing models. Usually, no attempt is made to identify sensitive parameters, and those that are used are often given "factory-supplied" default values, or values obtained from some non-systematic exploratory analysis. The immediate consequence of this is, of course, that it is not clear if better models could have been obtained if some form of parameter selection and optimisation had been performed. Questions follow inevitably on the experiments themselves: specifically, are all algorithms being treated fairly, and is the exploratory phase sufficiently well-defined to allow the experiments to be replicated? In this paper, we investigate the use of parameter selection and optimisation techniques grouped under the study of experimental design. Screening and response surface methods determine, in turn, sensitive parameters and good values for these parameters. Screening is done here by constructing a stepwise regression model relating the utility of an ILP system's hypothesis to its input parameters, using systematic combinations of values of input parameters (technically speaking, we use a two-level fractional factorial design of the input parameters). The parameters used by the regression model are taken to be the sensitive parameters for the system for that application. We then seek an assignment of values to these sensitive parameters that maximise the utility of the ILP model. This is done using the technique of constructing a local "response surface". The parameters are then changed following the path of steepest ascent until a locally optimal value is reached. This combined use of parameter selection and response surface-driven optimisation has a long history of application in industrial engineering, and its role in ILP is demonstrated using well-known benchmarks. The results suggest that computatio
The automatic interpretation of dense three-dimensional (3D) point clouds is still an open research problem. The quality and usability of the derived models depend to a large degree on the availability of highly struc...
详细信息
The automatic interpretation of dense three-dimensional (3D) point clouds is still an open research problem. The quality and usability of the derived models depend to a large degree on the availability of highly structured models which represent semantics explicitly and provide a priori knowledge to the interpretation process. The usage of formal grammars for modelling man-made objects has gained increasing interest in the last few years. In order to cope with the variety and complexity of buildings, a large number of fairly sophisticated grammar rules are needed. As yet, such rules mostly have to be designed by human experts. This article describes a novel approach to machine learning of attribute grammar rules based on the inductive logic programming paradigm. Apart from syntactic differences, logic programs and attribute grammars are basically the same language. Attribute grammars extend context-free grammars by attributes and semantic rules and provide a much larger expressive power. Our approach to derive attribute grammars is able to deal with two kinds of input data. On the one hand, we show how attribute grammars can be derived from precise descriptions in the form of examples provided by a human user as the teacher. On the other hand, we present the acquisition of models from noisy observations such as 3D point clouds. This includes the learning of geometric and topological constraints by taking measurement errors into account. The feasibility of our approach is proven exemplarily by stairs, and a generic framework for learning other building parts is discussed. Stairs aggregate an arbitrary number of steps in a manner which is specified by topological and geometric constraints and can be modelled in a recursive way. Due to this recursion, they pose a special challenge to machine learning. In order to learn the concept of stairs, only a small number of examples were required. Our approach represents and addresses the quality of the given observations and th
We present a novel approach to embodied learning of qualitative models. We introduce, algorithm STRUDEL that enables an autonomous robot to discover new concepts by performing experiments in its environment. The robot...
详细信息
We present a novel approach to embodied learning of qualitative models. We introduce, algorithm STRUDEL that enables an autonomous robot to discover new concepts by performing experiments in its environment. The robot collects data about its actions and its observations of the environment. Prom the obtained data, the robot lean is qualitative descriptive models of the effects that its actions have in the environment. Models are learned using inductive logic programming. We describe two experiments with a humanoid robot Nao in which Nao learns descriptive qualitative models which contain what can be interpreted as simple definitions of the concepts of movability and stability.
Background: Quantitative structure-activity relationships (QSAR) analysis of peptides is helpful for designing various types of drugs such as kinase inhibitor or antigen. Capturing various properties of peptides is es...
详细信息
Background: Quantitative structure-activity relationships (QSAR) analysis of peptides is helpful for designing various types of drugs such as kinase inhibitor or antigen. Capturing various properties of peptides is essential for analyzing two-dimensional QSAR. A descriptor of peptides is an important element for capturing properties. The atom pair holographic (APH) code is designed for the description of peptides and it represents peptides as the combination of thirty-six types of key atoms and their intermediate binding between two key atoms. Results: The substructure pair descriptor (SPAD) represents peptides as the combination of forty-nine types of key substructures and the sequence of amino acid residues between two substructures. The size of the key substructures is larger and the length of the sequence is longer than traditional descriptors. Similarity searches on C5a inhibitor data set and kinase inhibitor data set showed that order of inhibitors become three times higher by representing peptides with SPAD, respectively. Comparing scope of each descriptor shows that SPAD captures different properties from APH. Conclusion: QSAR/QSPR for peptides is helpful for designing various types of drugs such as kinase inhibitor and antigen. SPAD is a novel and powerful descriptor for various types of peptides. Accuracy of QSAR/QSPR becomes higher by describing peptides with SPAD.
inductive program synthesis addresses the problem of automatically generating (declarative) recursive programs from ambiguous specifications such as input/output examples. Potential applications range from software de...
详细信息
inductive program synthesis addresses the problem of automatically generating (declarative) recursive programs from ambiguous specifications such as input/output examples. Potential applications range from software development to intelligent agents that learn in recursive domains. Current systems suffer from either strong restrictions regarding the form of inducible programs or from blind search in vast program spaces. The main contribution of my dissertation (Kitzelmann, Ph.D. thesis, 2010) is the algorithm IGOR2 for the induction of functional programs. It is based on search in program spaces but derives candidate programs directly from examples, rather than using them as test cases, and thereby prunes many programs. Experiments show promising results.
I would like to first commend Dr. Grabellus and his colleagues for scientifically investigating the topic of isolated limb perfusion (ILP) in its application to soft tissue sarcoma (STS). In 2009, within the United St...
详细信息
I would like to first commend Dr. Grabellus and his colleagues for scientifically investigating the topic of isolated limb perfusion (ILP) in its application to soft tissue sarcoma (STS). In 2009, within the United States, an estimated 10,660 cases of soft tissue sarcoma of all locations were newly diagnosed with 3,820 estimated deaths [1]. Soft tissue sarcomas remain an uncommon tumor type that clinicians have continued to strive to better understand in order to offer optimal treatment. We have come to understand that STS requires a thoughtful and often multimodality approach that potentially involves surgery, radiation, and chemotherapy. To date, there has been a relative lack of literature directed at scientifically examining the mechanisms by which ILP may exert its effects upon STS. Currently, a majority of the present body of literature for ILP and STS has generally been outcomes based as it related to limb preservation due to the poor outcomes previously seen when amputation was the accepted historical treatment.
We introduce relational information gain, a refinement scoring function measuring the informativeness of newly introduced variables. The gain can be interpreted as a conditional entropy in a well-defined sense and can...
详细信息
We introduce relational information gain, a refinement scoring function measuring the informativeness of newly introduced variables. The gain can be interpreted as a conditional entropy in a well-defined sense and can be efficiently approximately computed. In conjunction with simple greedy general-to-specific search algorithms such as FOIL, it yields an efficient and competitive algorithm in terms of predictive accuracy and compactness of the learned theory. In conjunction with the decision tree learner TILDE, it offers a beneficial alternative to lookahead, achieving similar performance while significantly reducing the number of evaluated literals.
暂无评论