The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Imposing a markovian condition on the situation calculus enables the embedding of situation calculus theories into the DEVS (discrete event system specification) modelling and simulation framework. DEVS has an algebraic formalism relying on classical systems theory, and has been used to good effect in practical domains. The demonstrated correspondence between the situation calculus and DEVS is based...
This paper considers how theories containing theoretical terms can be tested with experiments which only give results in observable terms. An agent will be defined which makes predictions about the effect of actions. To test such an agent, the predictions of the agents will measured against an experimental frame. With the experimental frame a result will be presented demonstrating when some theoretical...
In the situation calculus states are often distinguished from situations by the assumption that situations are paths in a rooted tree while a state is a particular truth assignment to the fluents. It is then possible that two situations have end points that agree on all fluents, i.e., are the same state, and yet be distinct from the perspective of situations. This has the merit of making inductive...
We provide a characterization of plan recognition in terms of a general framework of belief revision and non-monotonic reasoning. We adopt a generalization of classical belief revision to describe a competence model of plan recognition which supports dynamic change to all aspects of a plan recognition knowledge base, including background knowledge, action descriptions and their relationship to named...
Conservative extensions of (classical) logical theories play an important role in software engineering, because they provide a formal basis for program refinement and guarantee the integrity and transparency of modules and objects. Similarly specification morphisms play a central role for information hiding and combining modules. Surprisingly, while the use of nonmonotonic theories for describing...
Normal forms play an important role in computer science, for example in the areas of logic and databases. This paper provides a study of normal forms for some prominent logics for default reasoning. In particular we show that in Constrained and in Justified Default Logic, semi-normal default theories can represent arbitrary default theories. The main result for Justified Default Logic requires the...
Defeasible Logic is a nonmonotonic reasoning approach which has an efficient implementation. Currently Defeasible Logic can only prove ground literals. We describe a version of Defeasible Logic which is capable of proving existentially and universally closed literals, as well as ground literals. The intuition motivating the formalism is presented, as are some of its properties.
Two approaches of pattern recognition for robotics applications are introduced. The first study is concerned with a method of efficient pattern classification for moving objects using a discriminant tree. The second study is about three dimensional pattern classifications. Both studies use fuzzy logic and hierarchical knowledge base. In the first study, the experimental system shows the robot-arm...
We present an adaptive curvature scale space technique for extracting symbolic topographical descriptions from image data such as that of three dimensional digital terrain maps where specific image interpretation constraints play a significant role in defining the scale of analysis. In our approach we use machine learning techniques to learn efficient segmentation of image data, the Topograph, which...
Video image analysis is able to provide quantitative data on postural and movement abnormalities and thus has an important application in neurological diagnosis and management. The conventional techniques require patients to be videoed while wearing markers in a highly structured laboratory environment. This restricts the utility of video in routine clinical practice. We have begun development of...
Vowel recognition is essential in Chinese speech recognition, especially in the speaker independent tasks. In this paper, the authors argued that the fixed length frame segmentation of the speech signal makes the feature extracting process lose essential features and introduces some irrelevant information so that the extracted features may be less expressive and consistent. Using the pitch-based dynamically...
Most people have no difficulty in picking out the beat in a piece of music, and even if they cannot define what it is they have detected, they can tap their feet in time with the music. This ability is called beat induction. A more difficult task, which we call rhythm recognition, involves uncovering the hierarchical structure of the timing relationships in the music, at a higher level than the frequencies...
Boosting techniques allow the combination of a collection of sequentially trained neural networks into an ensemble whose classification performance is superior to any of the individual neural networks. Empirical studies on the performance of boosting neural networks in optical character recognition have demonstrated significant improvements in classification. In this paper we report on an empirical...
We present an approach to concept discovery in machine learning based on searching for maximally general credible classifications. To be credible, a classification must provide decisions for all or nearly all possible values of the condition attributes, and these decisions must be adequately supported by evidence. Our objective is to find a classification for a domain that meets predefined quality...
As databases grow in size and complexity the task of adding value to the wealth of data becomes difficult. Data mining has emerged as the technology to add value to enormous databases by finding new and important snippets (or nuggets) of knowledge. With large training sets, however, extremely large collections of nuggets are being extracted, leading to much “fools gold” amongst which to fossick for...
Input-Output Agent Modelling (IOAM) is an approach to modelling an agent in terms of relationships. between the inputs and outputs of the cognitive system. This approach, together with a leading inductive learning algorithm, C4.5, has been adopted to build a subtraction skill modeller, C4.5-IOAM. It models agents' competencies with a set of decision trees. C4.5-IOAM makes no prediction when predictions...
An important problem in providing personalised recommendations is how to determine when the sample items are representative of the user's long-term preferences (user profile) and when more sample items must be collected before a profile pattern may be identified. In this paper, we present an algorithm to determine if a particular sample set for a user is sufficient to provide personalised recommendations...
This paper describes a system that performs hierarchical error recovery, and detects and corrects a single error in a sentence at the lexical, syntactic, and/or semantic levels. If the system is unable to repair an erroneous sentence on the assumption that it has a single error, a multiple error recovery system is invoked. The system employs a chart parsing algorithm and uses an augmented context-free...
Browsing is one of the most popular ways to gather information in database with hypertext structure. In the browsing, a user continuously searches nodes which include useful information for her/him. Her/his interests, then, often change while the browsing. We call this type of browsing “context-sensitive browsing” in order to distinguish it from browsing with consistent interests. In this paper, we...
I introduce the T-SOM, an unsupervised neural network model based on well-known Kohonen Self-Organizing Maps. This model adds to SOM-properties the next new characteristics : a multiresolution knowledge representation, a low complexity algorithm and a simplified learning parameters tuning. A T-SOM network is a data analysis tool specially efficient in large volume data processing. The real purpose...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.