32544015
Belief revision for adaptive negotiation agents
Existing negotiation agents are primitive in terms of what they can learn and how responsive they are towards the changing negotiation contexts. These weaknesses can be alleviated if an expressive representation language is used to represent negotiation contexts and a sound inference mechanism is applied to reason about the preferential changes arising in these negotiation contexts. This paper illustrates a novel adaptive negotiation agent model, which is underpinned by the well-known AGM belief revision logic. Our preliminary experiments show that the performance of the belief-based adaptive negotiation agents is promising.
34497095
3D Protein Structure Matching by Patch Signatures
For determining functionality dependencies between two proteins, both represented as 3D structures, it is an essential condition that they have one or more matching structural regions called patches. As 3D structures for proteins are large, complex and constantly evolving, it is computationally expensive and very time-consuming to identify possible locations and sizes of patches for a given protein against a large protein database. In this paper, we address a vector space based representation for protein structures, where a patch is formed by the vectors within the region. Based on our previews work, a compact representation of the patch named patch signature is applied here. A similarity measure of two patches is then derived based on their signatures. To achieve fast patch matching in large protein databases, a match-and-expand strategy is proposed. Given a query patch, a set of small k-sized matching patches, called candidate patches, is generated in match stage. The candidate patches are further filtered by enlarging k in expand stage. Our extensive experimental results demonstrate encouraging performances with respect to this biologically critical but previously computationally prohibitive problem.
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).
490406
Skyline Queries Against Mobile Lightweight Devices in MANETs
Skyline queries are well suited when retrieving data according to multiple criteria. While most previous work has assumed a centralized setting this paper considers skyline querying in a mobile and distributed setting, where each mobile device is capable of holding only a portion of the whole dataset; where devices communicate through mobile ad hoc networks; and where a query issued by a mobile user is interested only in the user’s local area, although a query generally involves data stored on many mobile devices due to the storage limitations. We present techniques that aim to reduce the costs of communication among mobile devices and reduce the execution time on each single mobile device. For the former, skyline query requests are forwarded among mobile devices in a deliberate way, such that the amount of data to be transferred is reduced. For the latter, specific optimization measures are proposed for resource-constrained mobile devices. We conduct extensive experiments to show that our proposal performs efficiently in real mobile devices and simulated wireless ad hoc networks.
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.
9615548
Dynamic Belief Hierarchies
Typically some of an agent's beliefs are more reliable than others. Consequently we give a hierarchical definition of belief, according to which an agent's beliefs form a coherent hierarchy and new beliefs are defined with reference to it. We then show how preferential entailment can be used to formalize the persistence and revision of belief hierarchies, and discuss the the relationship between our theory and the AGM theory of belief revision.
8291104
An enhanced framework for real-time hair animation
Maximizing visual effect is a major problem in real-time animation. A real-time hair animation framework was proposed previously by C. K. Koh and Z. Huang (2000, 2001) based on 2D representation and texture mapping. One problem is that it lacks of the volumetric effect due to its 2D nature. This paper presents a technique using the U-shape strip to solve the problem.