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Reviews some of the good reasons for looking to real neural nets for guidance on ways of implementing effective parallel computation. Discusses existing artificial neural nets with particular attention to the extent to which they model real neural activity. Indicates some serious mismatches, but shows that there are also important correspondences. The successful applications are to image processing, pattern classification and automatic optimization, in various guises. Reviews important issues raised by extension to the symbolic problem solving of “intellectual” thought, the prime concern of classical AI. These illustrate the importance of recursion, and of a degree of continuity associated with any evolutionary process.
Investigates the possibility of applying artificial intelligence to solve practical auditing problems faced by the public sector, namely the tax auditor of the Internal Revenue Services, when targeting firms for further investigation. Suggests that organizations which incorporate an operational artificial neural network system will raise their performance greatly. Proposes that the neural network will overcome problems faced by a direct knowledge acquisition method in building an expert system to preserve the expertise of senior auditors by the IRS in Taiwan. Provides an explanation of the neural network theory with regard to multi‐ and single‐layered neural networks. Statistics reveal the neural network performs favourably, and that three‐layer networks perform better than two‐layer neural networks. The results strongly suggest that neural networks can be used to identify firms requiring further auditing investigation, and also suggest future implications for intelligent auditing machines.
The usefulness of artificial neural nets stems from their ability to self‐adjust, or in some sense “learn”. In modern studies, the emphasis on powerful self‐organisation is less strong, but the early viewpoint is defended here as potentially useful. Possible extension of neural net capability to “symbolic” processing is related to Minsky’s “heuristic connection” and to Pask’s view of learning as necessarily involving reformulation of information in a new language. Relevance is demonstrated to the “Boxes” scheme of Michie and Chambers and recent developments in reinforcement learning.
The selection criteria for contractor pre‐qualification are characterized by the co‐existence of both quantitative and qualitative data. The qualitative data is…
The selection criteria for contractor pre‐qualification are characterized by the co‐existence of both quantitative and qualitative data. The qualitative data is non‐linear, uncertain and imprecise. An ideal decision support system for contractor pre‐qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated non‐linear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre‐qualification criteria (variables) were identified for the model. One hundred and twelve real pre‐qualification cases were collected from civil engineering projects in Hong Kong, and 88 hypothetical pre‐qualification cases were also generated according to the ‘If‐then’ rules used by professionals in the pre‐qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre‐qualification case consisted of input ratings for candidate contractors' attributes and their corresponding pre‐qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross‐validation was applied to estimate the generalization errors based on the ‘re‐sampling’ of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated non‐linear relationship between contractors' attributes and their corresponding pre‐qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre‐qualification task.
Attention is drawn to a principle of “significance feedback” in neural nets that was devised in the encouraging ambience of the Biological Computer Laboratory and is arguably fundamental to much of the subsequent practical application of artificial neural nets.
The background against which the innovation was made is reviewed, as well as subsequent developments. It is emphasised that Heinz von Foerster and BCL made important contributions prior to their focus on second‐order cybernetics.
The version of “significance feedback” denoted by “backpropagation of error” has found numerous applications, but in a restricted field, and the relevance to biology is uncertain.
Ways in which the principle might be extended are discussed, including attention to structural changes in networks, and extension of the field of application to include conceptual processing.
The original work was 40 years ago, but indications are given of questions that are still unanswered and avenues yet to be explored, some of them indicated by reference to intelligence as “fractal”.
This paper furthers work that already exists in the use of artificial intelligence techniques to forecast cost flow for construction projects. The paper explains the need…
This paper furthers work that already exists in the use of artificial intelligence techniques to forecast cost flow for construction projects. The paper explains the need for cost‐flow forecasting and investigates the methods currently used to perform such a task. It introduces neural networks as an alternative approach to the existing methods. The relationship between the number of nodes used and the accuracy of the neural network in modelling the cost flow is closely examined. From this research an optimal solution is proposed for the case and a prototype system is developed. The results of the investigation of the number of nodes used and testing of the prototype neural network for sample cases are presented and discussed.
The application of an artificial neural network (ANN) to forecast the construction duration of buildings at the predesign stage is described in this paper. A three‐layered…
The application of an artificial neural network (ANN) to forecast the construction duration of buildings at the predesign stage is described in this paper. A three‐layered back‐propagation (BP) network consisting of 11 input nodes has been constructed. Ten binary input nodes represent basic information on building features (i.e. building function, structural system, foundation, height, exterior finishing, quality of interior decorating, and accessibility to the site), and one real‐value input represents functional area. The input nodes are fully connected to one output node through hidden nodes. The network was implemented on a Pentium‐150 based microcomputer using a neurocomputer program written in C+ +. The Generalized Delta Rule (GDR) was used as learning algorithm. One hundred and thirty‐six buildings built during the period 1987–95 in the Greater Bangkok area were used for training and testing the network. The determination of the optimum number of hidden nodes, learning rate, and momentum were based on trial‐and‐error. The best network was found to consist of six hidden nodes, with a learning rate of 0.6, and null momentum. It was trained for 44700 epochs within 943 s such that the mean squared error (judgement) of training and test samples were reduced to 1.17 × 10−7 and 3.10 × 10−6, respectively. The network can forecast construction du‐ration at the predesign stage with an average error of 13.6%.
Recalls that the first proposal for artificial neural nets was more than half a century ago. Subsequent developments are reviewed, with particular attention to the interface between regulatory, continuous processing and symbol manipulation. Recalls the standpoint of McCulloch and Pitts, that artificial nets are not precise models but are potentially informative about living systems.
Modelling of the multiproject cash flow decisions in a contracting firm facilitates optimal resource utilization, financial planning, profit forecasting and enables the…
Modelling of the multiproject cash flow decisions in a contracting firm facilitates optimal resource utilization, financial planning, profit forecasting and enables the inclusion of cash‐flow liquidity in forecasting. However, a great challenge for contracting firm to manage his multiproject cash flow when large and multiple construction projects are involved (manipulate large amount of resources, e.g. labour, plant, material, cost, etc.). In such cases, the complexity of the problem, hence the constraints involved, renders most existing regular optimization techniques computationally intractable within reasonable time frames. This limit inhibits the ability of contracting firms to complete construction projects at maximum efficiency through efficient utilization of resources among projects. Recently, artificial neural networks have demonstrated its strength in solving many optimization problems efficiently. In this regard a novel recurrent‐neural‐network model that integrates multi‐objective linear programming and neural network (MOLPNN) techniques has been developed. The model was applied to a relatively large contracting company running 10 projects concurrently in Hong Kong. The case study verified the feasibility and applicability of the MOLPNN to the defined problem. A comparison undertaken of two optimal schedules (i.e. risk‐avoiding scheme A and risk‐seeking scheme B) of cash flow based on the decision maker's preference is described in this paper.
Traditionally, philosophers have ascribed moral agency almost exclusively to humans (Eshleman, 2004). Early writing about moral agency can be traced to Aristotle (Louden…
Traditionally, philosophers have ascribed moral agency almost exclusively to humans (Eshleman, 2004). Early writing about moral agency can be traced to Aristotle (Louden, 1989) and Aquinas (1997). In addition to human moral agents, Aristotle discussed the possibility of moral agency of the Greek gods and Aquinas discussed the possibility of moral agency of angels. In the case of angels, a difficulty in ascribing moral agency was that it was suspected that angels did not have enough independence from God to ascribe to the angels genuine moral choices. Recently, new candidates have been suggested for non‐human moral agency. Floridi and Sanders (2004) suggest that artificially intelligence (AI) programs that meet certain criteria may attain the status of moral agents; they suggest a redefinition of moral agency to clarify the relationship between artificial and human agents. Other philosophers, as well as scholars in Science and Technology Studies, are studying the possibility that artifacts that are not designed to mimic human intelligence still embody a kind of moral agency. For example, there has been a lively discussion about the moral intent and the consequential effects of speed bumps (Latour, 1994; Keulartz et al., 2004). The connections and distributed intelligence of a network is another candidate being considered for moral agency (Allen, Varner & Zinser, 2000). These philosophical arguments may have practical consequences for software developers, and for the people affected by computing. In this paper, we will examine ideas about artificial moral agency from the perspective of a software developer.