2933762
Simulating human intuitive decisions by Q-learning
Simulations have become a great tool for research in the natural sciences. However their potential has not been reached far enough in the social sciences. This is in part due to the difficulty in simulating human decision making and reproducing human-like behavior. Recent advances in neo-classical decision making have defined specific differences between the decision making capabilities of rational agents and humans as well as speculations into the cause. Presented is a Q-learning model for simulating human-like decision making based upon the intuition deliberation model proposed by psychologists Kahneman and Tversky. The model is tested against the classic economic bargaining game. In this game humans and rational agents consistently converge onto distinctly different strategies. Our experiments show that a selfish agent defers from the strategy of the rational agent and is more similar to human strategy.
35439746
Role-Based and Agent-Oriental Teamwork Modeling
1229519
A decision-theoretic approach for designing proactive communication in multi-agent teamwork
Techniques that support effective communication during teamwork processes are of particular importance. Psychological study shows that an effective team often can anticipate information exchange among the team and communicate relevant information proactively. Proactive communication is crucial for understanding and sharing common goals and for cooperative actions. Communication can be valuable if it assists agents with new and timely information; it also has cost because it consumes network resources such as bandwidth. To address these issues, we present a new model that uses information production and need to capture the complex multi-agent communication process and a dynamic decision-theoretic determination of communication strategies. We also introduce a generic utility function and an algorithm, DTPC (Decision-Theoretic Proactive Communication), that focuses on representing information production and need of team members and resolving decision interactions among them for making decisions.
14748203
A history sensitive cascade model in diffusion networks
Diffusion is a process by which information, viruses, ideas and new behavior spread over social networks. The traditional independent cascade model gives activated nodes a one-time chance to activate each of its neighboring nodes with some probability. This paper extends the traditional cascade model to be history dependent. We propose a new model called the History Sensitive Cascade Model (HSCM) that allows activated nodes to receive more than a one-time chance to activate their neighbors. HSCM provides 1) a polynomial algorithm for calculating the probability of activity for any arbitrary node at any arbitrary time in tree structure graphs, and 2) a Markov model for calculating the probability in general graphs. Finally, we perform an empirical study on HSCM under different network settings. These simulations have showed its power to observe and explain the emergent phenomena in the macro level when changing parameters in the micro level.
40423470
Proactive Communication in Observation-Based Agent Team Corporation
5679790
Proactive Information Exchange During Team Cooperation
We are concerned, ultimately, with multi-agent teams involving both software and human agents in which the software agents assist in training the humans, e.g., as virtual team members. The software agents must be capable of utilizing the teamwork mechanisms used by humans. In this paper, we describe how to give agents the same implicit communication capabilities, i.e., observation, that humans use. We show how agents can use observations of the environment and teammates' actions to estimate other agents beliefs without bothering them with unnecessary messages; we also show how agents can anticipate information needs among the team and proactively exchange information, reducing the total communication volume. To achieve these goals, we add sensing capabilities to agents and present algorithms that infer teammates' mental state from the observation. Experimental data is presented that shows that the advantage of observation in proactive information exchange as well as enhanced team performance.
38299104
A Computational Trust Model for Semantic Web Based on Bayesian Decision Theory
Enabling trust to ensure more effective and efficient agent interaction is at the heart of the Semantic Web vision. We propose a computational trust model based on Bayesian decision theory in this paper. Our trust model combines a variety of sources of information to assist users with making correct decision in choosing the appropriate providers according to their preferences that expressed by prior information and utility function, and takes three types of costs (operational, opportunity and service charges) into account during trust evaluating. Our approach gives trust a strict probabilistic interpretation and lays solid foundation for trust evaluating on the Semantic Web.
18909695
Ear Recognition using Improved Non-Negative Matrix Factorization
An improved non-negative matrix factorization with sparseness constraints (INMFSC) is proposed by imposing an additional constraint on the objective function of NMFSC, which can control the sparseness of both the basis vectors and the coefficient matrix simultaneously. The update rules to solve the objective function with constraints are presented. Research of ear recognition and its application is a new subject in the field of biometrics authentication. In practical application, ear is maybe partially occluded by hair etc. So the proposed INMFSC is applied on ear recognition with normal images and partially occluded images. Experiment results show that, compared with the traditional NMFSC, the proposed method not only obtains higher recognition rate, but also improves the sparseness and the orthogonality of coefficient matrix
481284
A Novel Method for Image Spam Filtering
As spamming morphs from text-only to multi-media in content, the existing text-based spam filters are no longer effective for the task. Image analysis technology will provide an effective tool to solve the above problem. This paper proposes a novel filter approach that focuses on the spam which change the content text to image files, or embed the spam message into attached images. Spam images are analyzed and specific features are selected for inspection based on components-based method. Then the network junk-mail-filter system can use these features to identify whether to filter the mail or not by feature matching. Experiments show that our method is efficient in inspecting document images with different geometric structure. The proposed method has potential applications in document image information extraction and filtering.
9609952
A survivable routing algorithm with differentiated domain protection based on a virtual topology graph in multi-domain optical networks
In this paper, we propose a novel differentiated domain protection (DDP) scheme to address survivability in multi-domain optical networks. Survivable routing in multi-domain networks is a challenging algorithmic problem because it requires choosing links that provide adequate protection both between domains and within domains that may offer different protection services. To satisfy the survivability requirements of different domains, DDP considers differentiated protection schemes, i.e., dedicated protection, shared protection, or no protection in different domains. In order to respect the principle of transparent management in multiple domains, DDP abstracts a multi-domain optical network to a virtual topology graph (VTG) for solving routing between domains. Simulation results show that DDP, unlike traditional schemes, can provide trade-off between protection methods and also allows better resource utilization.
42884670
Eulerian path methods for multiple sequence alignment
With the rapid increase in the size of genome sequence databases, the multiple sequence alignment problem is increasingly important and often requires the alignment of a large number of sequences. Beginning in 1975, many heuristic algorithms have been created to improve the speed of computation and the quality of alignment. We introduce a novel approach that is fundamentally distinct from all currently available methods. Our motivation comes from the Eulerian method for fragment assembly in DNA sequence determination, that transforms all the DNA sequencing fragments into a de Bruijn graph and then reduces sequence assembly to a Eulerian path problem.