17536375
Interactive Agents Learning Their Environment
In this paper we describe the implementation of interactive agents capable of gathering and extending their knowledge. Interactive agents are designed to perform tasks requested by a user in natural language. Using simple sentences the agent can answer questions and in case a task can not be fulfilled the agent must communicate with the user. In particular, an interactive agent can tell when necessary information for a task is missing, giving the user a chance to supply this information, which may in effect result in teaching the agent. The interactive agent platform is implemented in DLP, a tool for the implementation of 3D web agents. In this paper we discuss the motivation for interactive agents, the learning mechanisms and it’s realization in the DLP platform.
14446666
Awareness, Negation and Logical Omniscience
General Epistemic Logics suffer from the problem of logical omniscience, which is that an agent's knowledge and beliefs are closed under implication. There have been many attempts to solve the problem of logical omniscience. However, according to our intuition, sometimes an agent's knowledge and beliefs are indeed closed under implication. Based on the notion of awareness, we introduce two kinds of negations: general negation and strong negation. Moreover, four kinds of implications, general implication, strong implication, weak implication, and semi-strong implication, are introduced to correspond with the two kinds of negations. In our logics of regular awareness, explicit beliefs are not necessarily closed under general implication, which means that agents are not logically omniscient. However, explicit beliefs are closed under strong implication and semi-strong implication, which captures an intuitive closure property of beliefs.
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.
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.
607902
A Framework for Handling Inconsistency in Changing Ontologies
One of the major problems of large scale, distributed and evolving ontologies is the potential introduction of inconsistencies. In this paper we survey four different approaches to handling inconsistency in DL-based ontologies: consistent ontology evolution, repairing inconsistencies, reasoning in the presence of inconsistencies and multi-version reasoning. We present a common formal basis for all of them, and use this common basis to compare these approaches. We discuss the different requirements for each of these methods, the conditions under which each of them is applicable, the knowledge requirements of the various methods, and the different usage scenarios to which they would apply.
456837
Dynamic buffer management with extensible replacement policies
The objective of extensible DBMSs is to ease the construction of specialized DBMSs for nontraditional applications. Although much work has been done in providing various levels of extensibility (e.g., extensibility of data types and operators, query language extensibility, and query optimizer extensibility), there has been very limited research in providing extensibility at the buffer management level. Supporting extensibility at the buffer management level is important as it can contribute significantly to overall system performance.This paper addresses the problem of supporting extensibility of buffer replacement policies. The main contribution is the proposal of a framework for modeling buffer replacement policies. This work is novel in two aspects. First, by providing a uniform and generic specification of buffer replacement policies, the proposed framework unifies existing work in this area. Second, our work introduces a new level of extensibility. None of the existing extensible DBMSs, to our knowledge, provides extensibility at the buffer management level. The proposed framework provides a basis for the construction of an extensible buffer manager as part of a 100% Java-based storage manager. We conducted an extensive performance study to investigate the performance of the proposed framework. The experimental results demonstrate that the proposed framework is indeed feasible for existing DBMSs and improves system performance significantly without costing significant overhead.
1348972
Non-photorealistic rendering and content-based image retrieval
In this paper, we will show how non-photorealistic rendering (NPR) can take a new role in content-based image retrieval (CBIR). The proposed CBIR method applies a novel image similarity measure: unlike traditional features like color, texture, or shape, our measure is based on a painted representation of the original image. This is produced by a stochastic paintbrush algorithm which simulates a painting process. We use the stroke parameters (color, size, orientation, and location) as features and similarity is measured by matching strokes of a pair of images. The advantage of our approach is that it provides information not only about the color content but also about the structural properties of an image without the segmentation of the image. Experimental results show that the CBIR method using paintbrush features has higher retrieval rate than traditional methods using color or texture features only.
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.
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.
62635072
Kernel autoassociator with applications to visual classification
Autoassociator is an important issue in concept learning, and the learned concept of a particular class can be used to distinguish the class from the others. For nonlinear autoassociation, This work presents a new model referred to as kernel autoassociator. Using kernel feature space as a potential nonlinear manifold, the model formulates the autoassociation as a special reconstruction problem from kernel feature space to input space. Two methods are developed to solve the problem. We evaluate the autoassociator with artificial data, and apply it to handwritten digit recognition and multiview face recognition, yielding positive experimental results.