15420304
Incremental gradient descent imputation method for missing data in learning classifier systems
Learning with incomplete or missing data has been a major challenge in learning classifier system. One method for covering missing data is imputing missing values based on the statistic of known values. Another is marking them matching arbitrary case. A new approach using incremental gradient descent imputation model is proposed in this paper, which use the relationship among variables to estimate the missing value. And, some experiments are conducted in order to compare the performance of new approach and other classical covering methods.
10779366
An efficient adaptive focused crawler based on ontology learning
The enormous growth of the World Wide Web has made it important to perform resource discovery efficiently. Consequently, several new ideas have been proposed; among them a key technique is focused crawling which is able to crawl particular topical portions of the World Wide Web quickly without having to explore all Web pages. In this paper, we present an intelligent focused crawler algorithm in which we embed ontology to evaluate the page's relevance to the topic. Compared with other algorithms using domain knowledge, our algorithm can evolve the ontology automatically during crawl process. Considering the instinct characteristics of the ontology, propagation has also been imported to accelerate the evolution of the ontology. We applied our approaches in several tasks and provided an empirical evaluation which has shown promising results.
17713377
Feedback based Dynamic Autonomous Web Service Composition
Web services are becoming very important in applications from electronic commerce to application interoperation. While numerous efforts have focused on service composition, plan selection among similar plans with different performance has not been addressed. We propose a feedback based dynamic autonomous composition strategy for the automated composition of web services described in semantic Web. The strategy is tested in a simulated environment with good experimental results.
28841129
Believability Based Iterated Belief Revision
Classical iterated belief revision methods rarely take into account the impact of the uncertain information. In this paper, an approach of believability based iterated belief revision(BIBR) is presented. BIBR relates the belief revision in the multi-agent system to the believability of information, which plays an important role in the revision process. Based on the Dempster-Shafer theory of evidence and believability function formalism, the believability of information can be obtained. The revised belief set by BIBR is dependent on the history of revision, namely, on the information received prior to the current belief set. It is proved that the BIBR operation meets the AGM postulates for belief revision and the Darwiche and Pearl postulates for iterated belief revision.
10844784
Meta-game Equilibrium for Multi-agent Reinforcement Learning
This paper proposes a multi-agent Q-learning algorithm called meta-game-Q learning that is developed from the meta-game equilibrium concept Different from Nash equilibrium, meta-game equilibrium can achieve the optimal joint action game through deliberating its preference and predicting others' policies in the general-sum game A distributed negotiation algorithm is used to solve the meta-game equilibrium problem instead of using centralized linear programming algorithms We use the repeated prisoner's dilemma example to empirically demonstrate that the algorithm converges to meta-game equilibrium.
40250467
How to Choose the Optimal Policy in Multi-agent Belief Revision?
In multi-agent system, it is not enough to maintain the coherence of single agent's belief set only. By communication, one agent removing or adding a belief sentence may influence the certainty of other agents' belief sets. In this paper, we investigate carefully some features of belief revision process in different multi-agent systems and bring forward a uniform framework - Bereframe. In Bereframe, we think there are other objects rather than maintaining the coherence or maximize certainty of single belief revision process, and agent must choose the optimal revision policy to realize these. The credibility measure is brought forward, which is used to compute the max credibility degree of the knowledge background. In cooperative multi-agent system, agents always choose the combinative policy to maximize the certainty of whole system's belief set according to the welfare principle. And in semi-competitive multi-agent system, agents choose the revision policy according to the Pareto efficiency principle. In competitive multi-agent system, the Nash equilibrium criterion is applied into agent's belief revision process.
2955067
Single model face database retrieval by directional corner points
This paper presents a new face image retrieval approach using directional corner points (DCPs). Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching scheme suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be concerned. This research demonstrates that the proposed DCP approach provides a new solution, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.
17124128
FCAN: A Structured P2P System Based on Content Query
The existing DHT-based file storage systems are suffering problems of being restricted to exact match and lacking semantic-based query. To solve these problems, a DHT-based file storage system named FCAN is proposed. Mapping a document vector from semantic space to P2P space by FastMap, FCAN can effectively reduce the dimensions of it to implement semantic-based query without affecting the semantic integrality of this document. By clustering the nearest neighbors based on centroid, FCAN is further improved to reduce the redundant messages and search time. Simulations show that FCAN has not only higher recall and precision, but also fewer search time and redundant messages
14459995
Phase Transitions of Dominating Clique Problem and Their Implications to Heuristics in Satisfiability Search
We study a monotone NP decision problem, the dominating clique problem, whose phase transition occurs at a very dense stage of the random graph evolution process. We establish the exact threshold of the phase transition and propose an efficient search algorithm that runs in super-polynomial time with high probability. Our empirical studies reveal two even more intriguing phenomena in its typical-case complexity: (1) the problem is "uniformly hard" with a tiny runtime variance on negative instances. (2) Our algorithm and its CNF-tailored implementation, outperform several SAT solvers by a huge margin on dominating cliques and some other SAT problems with similar structures.
2287582
Resolution complexity of random constraint satisfaction problems: Another half of the story
Let C"n","m^2^,^k^,^t be a random constraint satisfaction problem (CSP) on n binary variables, where m constraints are selected uniformly at random from all the possible k-ary constraints each of which contains exactly t tuples of the values as its restrictions. We establish an upper bound on the constraint tightness threshold for C"n","m^2^,^k^,^t to have an exponential resolution complexity. The upper bound partly answers the open problem regarding the CSP resolution complexity with the tightness between the existing upper and lower bounds [D. Mitchell, Resolution complexity of random constraints, in: Proceedings Principles and Practices of Constraint Programming-CP 2002, Springer, Berlin, 2002, pp. 295-309].
45735125
Robust visual similarity retrieval in single model face databases
In this paper, we introduce a novel visual similarity measuring technique to retrieve face images in photo album databases for law enforcement. Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching schemes suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be considered. This paper presents a robust face retrieval approach using structural and spatial point correspondence in which the directional corner points (DCPs) are generated for efficient face coding and retrieval. A complete investigation on the proposed method is conducted, which covers face retrieval under controlled/ideal condition, scale variations, environmental changes and subject actions. The system performance is compared with the performance of the eigenface method. It is an attractive finding that the proposed DCP retrieval technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that the proposed DCP approach provides a new way, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.