Discovering the events expert practitioners extract from dynamic data streams: the modified unit marking technique

One of the cornerstones of expert performance in complex domains is the ability to perceive problem situations in terms of their task-relevant semantic properties. One such class of properties consists of phenomena that are defined in terms of patterns of change over time, i.e., events. A basic pre-requisite for working towards tools to support event recognition is a method for understanding the events that expert practitioners find meaningful in a given field of practice. In this article we present the modified unit marking procedure (mUMP), a technique adapted from work on social perception to facilitate identification of the meaningful phenomena which observers attend to in a dynamic data array. The mUMP and associated data analysis techniques are presented with examples from a first of a kind study where they were used to elicit and understand the events practitioners found meaningful in a scenario from an actual complex work domain.

Handling distributed authorization with delegation through answer set programming

Distributed authorization is an essential issue in computer security. Recent research shows that trust management is a promising approach for the authorization in distributed environments. There are two key issues for a trust management system: how to design an expressive high-level policy language and how to solve the compliance-checking problem (Blaze et al. in Proceedings of the Symposium on Security and Privacy, pp. 164–173, 1996; Proceedings of 2nd International Conference on Financial Cryptography (FC’98). LNCS, vol.1465, pp. 254–274, 1998), where ordinary logic programming has been used to formalize various distributed authorization policies (Li et al. in Proceedings of the 2002 IEEE Symposium on Security and Privacy, pp. 114–130, 2002; ACM Trans. Inf. Syst. Secur. (TISSEC) 6(1):128–171, 2003). In this paper, we employ Answer Set Programming to deal with many complex issues associated with the distributed authorization along the trust management approach. In particular, we propose a formal authorization language providing its semantics through Answer Set Programming. Using language , we cannot only express nonmonotonic delegation policies which have not been considered in previous approaches, but also represent the delegation with depth, separation of duty, and positive and negative authorizations. We also investigate basic computational properties related to our approach. Through two case studies. we further illustrate the application of our approach in distributed environments.     

Influence-based model decomposition for reasoning about spatially distributed physical systems

Many important science and engineering applications, such as regulating the temperature distribution over a semiconductor wafer and controlling the noise from a photocopy machine, require interpreting distributed data and designing decentralized controllers for spatially distributed systems. Developing effective computational techniques for representing and reasoning about these systems, which are usually modeled with partial differential equations (PDEs), is one of the major challenge problems for qualitative and spatial reasoning research.

This paper introduces a novel approach to decentralized control design, influence-based model decomposition, and applies it in the context of thermal regulation. Influence-based model decomposition uses a decentralized model, called an influence graph, as a key data abstraction representing influences of controls on distributed physical fields. It serves as the basis for novel algorithms for control placement and parameter design for distributed systems with large numbers of coupled variables. These algorithms exploit physical knowledge of locality, linear superposability, and continuity, encapsulated in influence graphs representing dependencies of field nodes on control nodes. The control placement design algorithms utilize influence graphs to decompose a problem domain so as to decouple the resulting regions. The decentralized control parameter optimization algorithms utilize influence graphs to efficiently evaluate thermal fields and to explicitly trade off computation, communication, and control quality. By leveraging the physical knowledge encapsulated in influence graphs, these control design algorithms are more efficient than standard techniques, and produce designs explainable in terms of problem structures.     

基于IBL算法的CBR系统中索引与检索机制研究

文章介绍了基于示例学习算法IBL的概况,并对其加以改进,提出了一个新的算法IBL－Cluster。它主要由概念描述形成算法和概念描述修改算法构成。在此基础上建立了应用在基于事例的推理系统CBR中的基于IBL算法的索引与检索机制。实验表明新算法IBL－Cluster在存储空间及测试正确率方面均有改善。     

Model-based cognitive diagnosis

This paper considers the problem of cognitive diagnosis as an instance of general diagnosis, as studied in artificial intelligence. Cognitive diagnosis is the process of inferring a cognitive state from observations of performance. It is thus a key component of any system which attempts to build a dynamic model of the user of that system. Many issues in cognitive diagnosis, previously discussed informally, are mapped onto formal techniques, with consequent increased clarity and rigour. But it is concluded that the general theories for diagnosis must be broadened to fully encompass the problems of cognitive diagnosis.     

Reasoning about networks of temporal relations and its applications to problem solving

An interval algebra (IA) has been proposed as a model for representing and reasoning about qualitative temporal relations between time intervals. Unfortunately, reasoning tasks with IA that involve deciding the satisfiability of the temporal constraints, or providing all the satisfying instances of the temporal constraints, areNP-complete. That is, solving these problems are computationally exponential in the worst case. However, several directions in improving their computational performance are still possible. This paper presents a new backtracking algorithm for finding a solution called consistent scenario. This algorithm has anO(n ³) best-case complexity, compared toO(n ⁴) of previous known backtrack algorithms, wheren denotes the number of intervals. By computational experiments, we tested the performance of different backtrack algorithms on a set of randomly generated networks with the results favoring our proposal. In this paper, we also present a new path consistency algorithm, which has been used for finding approximate solutions towards the minimal labeling networks. The worst-case complexity of the proposed algorithm is stillO(n ³); however, we are able to improve its performance by eliminating the unnecessary duplicate computation as presented in Allen's original algorithm, and by employing a most-constrained first principle, which ensures a faster convergence. The performance of the proposed scheme is evaluated through a large set of experimental data.     

Emergent Properties of a Market-based Digital Library with Strategic Agents

The University of Michigan Digital Library (UMDL) is designed as an open system that allows third parties to build and integrate their own profit-seeking agents into the marketplace of information goods and services. The profit-seeking behavior of agents, however, risks inefficient allocation of goods and services, as agents take strategic stances that might backfire. While it would be good if we could impose mechanisms to remove incentives for strategic reasoning, this is not possible in the UMDL. Therefore, our approach has instead been to study whether encouraging the other extreme—making strategic reasoning ubiquitous—provides an answer.Toward this end, we have designed a strategy (called the p-strategy) that uses a stochastic model of the market to find the best offer price. We have then examined the collective behavior of p-strategy agents in the UMDL auction. Our experiments show that strategic thinking is not always beneficial and that the advantage of being strategic decreases with the arrival of equally strategic agents. Furthermore, a simpler strategy can be as effective when enough other agents use the p-strategy. Consequently, we expect the UMDL is likely to evolve to a point where some agents use simpler strategies and some use the p-strategy.     

Using an induction prover for verifying arithmetic circuits

We show that existing theorem proving technology can be used effectively for mechanically verifying a family of arithmetic circuits. A theorem prover implementing: (i) a decision procedure for quantifier-free Presburger arithmetic with uninterpreted function symbols; (ii) conditional rewriting; and (iii) heuristics for carefully selecting induction schemes from terminating recursive function definitions; and (iv) well integrated with backtracking, can automatically verify number-theoretic properties of parameterized and generic adders, multipliers and division circuits. This is illustrated using our theorem prover Rewrite Rule Laboratory (RRL). To our knowledge, this is the first such demonstration of the capabilities of a theorem prover mechanizing induction. The above features of RRL are briefly discussed using illustrations from the verification of adder, multiplier and division circuits. Extensions to the prover likely to make it even more effective for hardware verification are discussed. Furthermore, it is believed that these results are scalable, and the proposed approach is likely to be effective for other arithmetic circuits as well.