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.
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.
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.
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.
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.
13349953
Heuristics and normative models of judgment under uncertainty
Abstract Psychological evidence shows that probability theory is not a proper descriptive model of intuitive human judgment. Instead, some heuristics have been proposed as such a descriptive model. This paper argues that probability theory has limitations even as a normative model. A new normative model of judgment under undertainty is designed under the assumption that the system's knowledge and resources are insufficient with respect to the questions that the system needs to answer. The proposed heuristics in human reasoning can also be observed in this new model, and can be justified according to the assumption.
44750839
Relating the Entropy of Joint Beliefs to Multi-agent Coordination
Current approaches to activity coordination in multi-agent systems (teams) range from strictly top down (plan-based coordination) to purely emergent (reactive coordination), with many hybrid variants, each having its specific advantages and disadvantages. It appears to be extremely difficult to rigorously compare various hybrid approaches to multi-agent coordination (and communication), given the lack of a generic semantics or some guidelines. In this paper, we studied some intuitive inter-agent communication policies and characterised them in terms of generic information-theoretic properties. In particular, the relative entropy of joint beliefs was suggested as an indicator of teams coordination potential. Our novel behaviour-based agent architecture (based on the Deep Behaviour Projection framework) enabled consistent reasoning about belief change, including beliefs about other agents. This allowed us to examine some of the identified communication policies empirically. The obtained results confirmed that there are certain interesting invariants – in particular, a change in team coordination (and overall performance) was shown to be within the boundaries indicated by the relative information entropy.
41586583
3D Articulated Object Understanding, Learning, and Recognition from 2D Images
This paper is aimed at 3D object understanding from 2D images, including articulated objects in active vision environment, using interactive, and internet virtual reality techniques. Generally speaking, an articulated object can be divided into two portions: main rigid portion and articulated portion. It is more complicated that "rigid" object in that the relative positions, shapes or angles between the main portion and the articulated portion have essentially infinite variations, in addition to the infinite variations of each individual rigid portions due to orientations, rotations and topological transformations. A new method generalized from linear combination is employed to investigate such problems. It uses very few learning samples, and can describe, understand, and recognize 3D articulated objects while the objects status is being changed in an active vision environment.
8428215
A Tennis Video Indexing Approach Through Pattern Discovery in Interactive Process
Tennis game is a mutual and interactive process between the two opponents, and audiences are usually interested in how the players oppose and compete with each other. However, previous works on tennis video indexing are mainly based on analyses of player’s behavior, such as movement and gesture, and seldom provide information of the match process. In this paper, a novel approach is proposed for tennis video indexing by mining the salient patterns in the match process. The match is first characterized by a time sequence of players’ joint movements, based on which an unsupervised way is performed to extract the sub-sequences with high occurrence frequencies. These sub-sequences, called patterns, reflect the technical styles and tactics of the players, and offer an effective manner to organize the video. Evaluations on four hour broadcasting tennis videos show very promising results.
17913196
Community Learning in Information Technology Fashion
In their striving to learn about information technology innovations, organizations draw on knowledge resources available in the diverse organizational communities that converge around those innovations. But even as such organizations learn about an innovation, so too does the larger community. Community learning takes place as its members reflect upon their learning and contribute their experiences, observations, and insights to the community’s on-going innovation discourse. Community learning and organizational learning thus build upon one another in a reciprocal process, or cycle, over time, as the stock of interpretations, adoption rationales, implementation strategies, and utilization patterns is expanded and refined. Relative to this overall cycle, we explore the neglected aspect that concerns how community learning draws on organizational learning. Analyzing the community discourse on enterprise resource planning (ERP) over the past 14 years, we found that different types of organizational actors played different roles, at different times, in contributing different types of knowledge to the discourse. Research analysts and technology vendors took leadership early on in articulating the know-what (conceptualization and interpretation) and know-why (justification) for ERP. Later on, adopters came to dominate the discourse through contributions of know-how (capacitation). We situate these observations in a larger model of the learning cycle, and outline a number of areas for future research on the crucial interactions between organizational and community learning in IT innovation.
6309219
Contextual browsing for highlights in sports video
A novel highlight browsing approach for sports videos, based on contextual cue categorization, is proposed in this paper. Contextual cues are those salient audio and visual cues around highlight segments, and they are also informative and attractive to audiences. With further analysis and categorization of these contextual cues, the user could access highlights more flexibly and effectively. An experimental system is built for broadcasting diving videos in our current work, in which contextual cues such as slow-motion replays and game statistics information are extracted and categorized for highlight browsing. An efficient algorithm for slow-motion replay detection and categorization in diving videos is also proposed. Experimental results on 2 hours broadcasting diving videos have demonstrated the effectiveness and efficiency of the proposed approach.