5826256
Confidence as Higher-Order Uncertainty
With conflicting evidence, a reasoning system derives uncertain conclusions. If the system is open to new evidence, it faces additionally a higher-order uncertainty, because the first-order uncertainty evaluations are uncertain themselves — they can be changed by future evidence. A new measurement, confidence, is introduced for this higher-order uncertainty. It is defined in terms of the amount of available evidence, and interpreted and processed as the relative stability of the first-order uncertainty evaluation. Its relation with other approaches of “reasoning with uncertainty” is also discussed.
34131767
Problem Solving With Insufficient Resources
A new approach, "controlled concurrency," is introduced for inference control in an adaptive reasoning system working with insufficient knowledge and resources. With this method, a problem-solving process is constructed from atomic steps in run time, according to the system's past experience and the current context. The system carries out many such processes in parallel by distributing its resources among them, and dynamically adjusting the distribution according to feedback. A data structure, "bag," is designed to support this dynamic time-space allocation, and is a kind of probabilistic priority queue. This approach provides a flexible, efficient, and adaptive control mechanism for real-time systems working with uncertain knowledge. To analyze problem solving in such a system, the traditional computability theory and computational complexity theory become inappropriate, because the system no longer follows problem-specific algorithms in problem solving.
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.
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.
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.
11922333
Parallel Matching of 3D Articulated Object Recognition
In dealing with large volume image data, sequential methods usually are too slow and unsatisfactory. This paper introduces a new system employing parallel matching in high-level recognition of 3D articulated objects. A new structural strategy using linear combination and parallel graphic matching techniques is presented for 3D polyhedral objects representable by 2D line-drawings. It solves one of the basic concerns in diffusion tomography complexities, i.e. patterns can be reconstructed through fewer projections, and 3D objects can be recognized by a few learning sample views. It also improves some of the current methods while overcoming their drawbacks. Furthermore, it can distinguish very similar objects and is more accurate than other methods in the literature. An online webpage system for understanding and recognizing 3D objects is also illustrated.
6354609
A Genetic Algorithm for VLSI Floorplanning
We present a genetic algorithm (GA) which uses a normalized postfix encoding scheme to solve the VLSI floorplanning problem. We claim to have overcome the representational problems previously associated with encoding postfix expressions into GAs, and have developed a novel encoding system which preserves the integrity of solutions under all the genetic operators. Optimal floorplans are obtained for module sets taken from some MCNC benchmarks. The slicing tree construction process, used by our GA to generate the floorplans, has a run time scaling which compares very favourably with other recent approaches.
38659056
Intelligent Pattern Recognition and Applications
28403175
GI/S: A Graphical User Interface For Symbolic Computation Systems
The design and implementation of GI/S, a Graphical user Interface for Symbolic computation systems, is described. The system provides a multiple window environment for the high-resolution 2-D display of mathematical expressions, the ability to select and manipulate parts of expressions with a mouse, and graphics plotting of mathematical expressions. GI/S also provides command line editing and a history mechanism for editing and re-executing past commands. GI/S is written in Franz Lisp for the Macsyma system and has been implemented on the Tektronix 4404 workstation.
15398175
Hardware-based Packet Classification Made Fast and Efficient
To achieve fast packet classification, a hardware-based scheme, cross-producting recurrence (CPR), based on a formerly proposed cross-producting scheme is proposed. This scheme simplifies the classification procedure and decrease the distinct combinations of fields by hierarchically decomposing the multi-dimensional space. In the new scheme, the multi-dimensional space is endowed with a hierarchical property which self-divides into several smaller subspaces, whereas the procedure of packet classification is translated into repeatedly searching for matching subspaces. The required storage of the proposed scheme is significantly reduced since the distinct fields of subspaces is controlled by a pre-defined configuration and can be much less than that of the filters. The experimental results demonstrate the effectiveness and scalability of the proposed scheme