12410756
Reasoning with Multi-version Ontologies: A Temporal Logic Approach
In this paper we propose a framework for reasoning with multi-version ontology, in which a temporal logic is developed to serve as its semantic foundation. We show that the temporal logic approach can provide a solid semantic foundation which can support various requirements on multi-version ontology reasoning. We have implemented the prototype of MORE (Multi-version Ontology REasoner), which is based on the proposed framework. We have tested MORE with several realistic ontologies. In this paper, we also discuss the implementation issues and report the experiments with MORE.
38873444
Logics for Belief Dependence
In this paper, we investigate the theoretical foundations of belief dependence in multiple agent environment, where agents may rely on someone else about their beliefs or knowledge. Several logics for belief dependence are introduced and studied. First of all, we try to formalize the problem of belief dependence in the framework of general epistemic logics, by which we will argue that general epistemic logic is not appropriate to formalize the problem of belief dependence. Then, based on an approach which is similar to Fagin and Halpern's general awareness logic, we present the second logic for belief dependence, which is called a syntactic approach. The third logic is an adapted possible world logic, where sub-beliefs are directly introduced in the models.
1230191
3D agent-based virtual communities
In this paper we propose an approach to 3D agent-based virtual communities in which autonomous agents are participants in VRML-based virtual worlds to enhance the interaction with users or serve as intelligent navigation assistants. In addition, an agent communication language (ACL) is designed as a high-level communication facility, in particular for the realization of shared objects in virtual communities. As a typical example of 3D agent-based virtual communities, a VRML-based multi-user soccer game has been developed and implemented in the distributed logic programming language DLP. We discuss how DLP can be used for the implementation of 3D agent-based virtual communities.
19558398
Gestures for Embodied Agents with Logic Programming
The paper discusses how distributed logic programming can be used to define and control the hand gestures of embodied agents in virtual worlds, by using the STEP language as an interface between the constructs of logic programming and the humanoid model defined in Virtual Reality Modelling Language (VRML). By using this framework, different gesture dictionaries can be defined and variants of a hand gesture, according to dynamically changing factors, can be generated on the fly. The approach is tested on the demanding demonstrator of conducting, providing experience, also on time performance of the animation.
18313204
A taxonomy of web agents
We propose a taxonomy of Web agents, which encompasses agents that provide a text-based interface to, for example, information retrieval services as well as avatar-embodied guides that help visitors to navigate in virtual environments. Our taxonomy must be regarded as an instrument to delineate targets for research and the realization of prototype applications that demonstrate the usefulness of agent-based intelligence on the Web. In addition, we deploy our agent-taxonomy to establish the implications particular target applications have with respect to software architecture and computational resources.
13610294
Seeing is believing: A common sense theory of the adoption of perception-based beliefs
In this paper we present a formal common sense theory of the adoption of perception-based beliefs. We begin with a logical analysis of perception and then consider when perception should lead to belief change. Our theory is intended to apply to perception in humans and to perception in artificial agents at the level of the symbolic interface between a vision system and a belief system. In order to provide a context for our work we relate it to the emerging field of cognitive robotics, give an abstract architecture for an agent which is both embodied and capable of reasoning, and relate this to the concrete architectures of two vision-based surveillance systems.
16942431
A graph model for E-commerce recommender systems
Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.
4872605
Progressive transmission and rendering of foveated volume data
With development of biomedical and networking technology, interactive volume rendering with data transmitted via the network becomes an interesting topic. Two technical problems are data transmission and fast volume rendering. In this paper, two schemes using Wavelet foveation are proposed and implemented: the region-based and coarse-to-fine. The first scheme transmits and renders the region of interest (ROI) of volume data on the client site at highest resolution for the first time, and consequently it iteratively expands the region layer by layer towards to the peripheral. While the second one gives a complete image in a low resolution for the first time on the client. The detail coefficients will be transmitted continuously and the rendering result will be progressively refined depending on the distance to the ROI.
16206286
Classifying Document Titles Based on Information Inference
We propose an intelligent document title classification agent based on a theory of information inference. The information is represented as vectorial spaces computed by a cognitively motivated model, namely Hyperspace Analogue to Language (HAL). A combination heuristic is used to combine a group of concepts into one single combination vector. Information inference can be performed on the HAL spaces via computing information flow between vectors or combination vectors. Based on this theory, a document title is treated as a combination vector by applying the combination heuristic to all the non-stop terms in the title. Two methodologies for learning and assigning categories to document titles are addressed. Experimental results on Reuters-21578 corpus show that our framework is promising and its performance achieves 71% of the upper bound (which is approximated by using whole documents).
7757717
A kernel autoassociator approach to pattern classification
Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.
15320041
Feature extraction of volume data based on multi-scale representation
In this paper, we present a novel algorithm that can detect and extract salient points as features in 3-D volume datasets. This algorithm extracts not only the locations of feature points, but also finds out the scales at which the features are most significant. It applies the scale-space theory by adaptively processing the input volume in a few discrete scales. The features points, as well as their scales, detected can be used for subsequent processing, such as content-based volume data authentication and content-based volume rendering.We have implemented the algorithm and tested its effectiveness on several volume datasets. Some optimization has also been done on time-consuming intermediate steps to speed up the feature detection process.