13042163
Non-Axiomatic Reasoning System (Version 4.1)
NARS (Non-Axiomatic Reasoning System) is an intelligent reasoning system. It answers questions according to the knowledge originally provided by its user. What makes it different from conventional reasoning systems is its ability to learn from its experience and to work with insufficient knowledge and resources. The NARS 4.1 demo is a Java applet. It comes with help information and simple examples to show how the system does deduction, induction, abduction, analogy, belief revision, membership evaluation, relational inference, backward inference, new concept formation, and so on, in a unified manner. The demo also allows its user to create new examples to test the system, as well as to see the internal structure and process when the system is running. The on-line help document contains links to relevant publications. A previous version of the system, NARS 3.0, is described in detail in (Wang, 1995), which, and other related publications, are available at the author’s web page.
785488
The limitation of Bayesianism
In the current discussion about the capacity of Bayesianism in reasoning under uncertainty, there is a conceptual and notational confusion between the explicit condition and the implicit condition of a probability evaluation. Consequently, the limitation of Bayesianism is often seriously underestimated. To represent the uncertainty of a belief system where revision is needed, it is not enough to assign a probability value to each belief.
26841509
From inheritance relation to nonaxiomatic logic
Abstract A nonaxiomatic reasoning system is an adaptive system that works with insufficient knowledge and resources. At the beginning of the paper, three binary term logics are defined. The first is based only on an inheritance relation. The second and the third suggest a novel way to process extension and intension, and they also have interesting relations with Aristotle's syllogistic logic. Based on the three simple systems, a nonaxiomatic logic is defined. It has a term-oriented language and an experience-grounded semantics. It can uniformly represent and process randomness, fuzziness, and ignorance. It can also uniformly carry out deduction, abduction, induction, and revision.
10740837
A logic of categorization
The reasoning system known as NARS constitutes a model of categorization. NARS is designed to be an adaptive system that works under the constraint of insufficient knowledge and resources. It consists of a categorical language, an experience-grounded semantics, a set of syllogistic inference rules, a dynamic memory structure, and a control mechanism that manages asynchronized parallel inference. In the system, reasoning and categorization are two aspects of the same underlying process. As a model of categorization, NARS unifies several existing theories.
2060430
A Defect in Dempster-Shafer Theory
By analyzing the relationships among chance, weight of evidence and degree of belief, we show that the assertion "probability functions are special cases of belief functions" and the assertion "Dempster's rule can be used to combine belief functions based on distinct bodies of evidence" together lead to an inconsistency in Dempster-Shafer theory. To solve this problem, we must reject some fundamental postulates of the theory. We introduce a new approach for uncertainty management that shares many intuitive ideas with D-S theory, while avoiding this problem.
28879338
Reasoning in Practical Situations
An automatic reasoning system usually consists of the following major components: (1) a formal language that represents knowledge, (2) a semantics that defines meaning and truth value in the language, (3) a set of inference rules that derives new knowledge from existing knowledge, (4) a memory that stores knowledge, and (5) a control mechanism that chooses premises and rules in each inference step. The first three components are usually referred to as a logic, or the logical part of the reasoning system, and the last two as an implementation of, or the control part of the system.
10322452
SUI: a system independent user interface for an integrated scientific computing environment
The design and implementation of a Scientific User Interface is presented. Written in the C language, SUI is a window-menu-mouse oriented graphical user interface that is designed to provide a modern and integrated computing environment for scientific work. SUI can serve multiple client systems in parallel including symbolic, numeric, graphics and document formatting systems. SUI achieves hardware and operating system independence as well as network transparency by employing the X11 protocols and achieves client system independence by defining a client-SUI protocol that is simple and effective. Features of SUI includes input editing, history, 2-D mathematical expression display, interactive selection of subexpressions, interactive display and manipulation of 2-D and 3-D plots of mathematical functions, cut and paste with syntax translation, command templates, incremental 2-D display of mathematical input, and interactive configuration. A prototype system demonstration is planned for ISSAC'90
17343502
IAMC architecture and prototyping: a progress report
Internet Accessible Mathematical Computation (IAMC) is a distributed framework to supply mathematical computing powers over the Internet. Presented are conceptual and experimental work on the IAMC architecture, a client prototype (Dragonfly), client GUI, a server prototype (Starfish), the Mathematical Computation Protocol (MCP), mathematical data encoding, and the external compute engine interface.
15177304
A new method of color image segmentation based on intensity and hue clustering
A new method of color image segmentation is proposed. It is based on the K-means algorithm in HSI color space and has the advantage over those based on the RGB space. Both the hue and the intensity components are fully utilized. In the process of hue clustering, the special cyclic property of the hue component is taken into consideration. The paper gives the definition of the distance and the center in the hue space, based on which the hue-clustering algorithm is implemented. Utilised in medical image processing, the new method gives a good performance.
62548937
Performance Modeling and Prediction of Face Recognition Systems
It is a challenging task to accurately model the performance of a face recognition system, and to predict its individual recognition results under various environments. This paper presents generic methods to model and predict the face recognition performance based on analysis of similarity measurement. We first introduce a concept of "perfect recognition", which only depends on the intrinsic structure of a recognition system. A metric extracted from perfect recognition similarity scores (PRSS) allows modeling the face recognition performance without empirical testing. This paper also presents an EM algorithm to predict the recognition rate of a query set. Furthermore, features are extracted from similarity scores to predict recognition results of individual queries. The presented methods can select algorithm parameters offline, predict recognition performance online, and adjust face alignment online for better recognition. Experimental results show that the performance of recognition systems can be greatly improved using presented methods.
8406634
Cycles in 2-connected graphs
Let Gn be a class of graphs on n vertices. For an integer c, let ex(gn, c) be the smallest integer such that if G is a graph in Gn with more than ex(Gn, c) edges, then G contains a cycle of length more than c. A classical result of Erdos and Gallai is that if Gn is the class of all simple graphs on n vertices, then ex(Gn, c) = c/2 (n - 1). The result is best possible when n - 1 is divisible by c - 1, in view of the graph consisting of copies of Kc all having exactly one vertex in common. Woodall improved the result by giving best possible bounds for the remaining cases when n - 1 is not divisible by c - 1, and conjectured that if Gn is the class of all 2-connected simple graphs on n vertices, then ex(Gn, c) = max{f(n,2,c), f(n, ⌊c/2 ⌋,c)}, where f(n, t, c) = (c+1-t 2) + t(n - c - 1 + t), 2 ≤ t ≤ c/2, is the number of edges in the graph obtained from Kc+1-t by adding n-(c + 1 - t) isolated vertices each joined to the same t vertices of Kc+1-t. By using a result of Woodall together with an edge-switching technique, we confirm Woodall's conjecture in this paper.