The Discrete-Time Bulk-Service Geo/Geo/1

This paper deals with a discrete-time bulk-service Geo/Geo/1 queueing system with infinite buffer space and multiple working vacations. Considering an early arrival system, as soon as the server empties the system in a regular busy period, he leaves the system and takes a working vacation for a random duration at time n. The service times both in a working vacation and in a busy period and the vacation times are assumed to be geometrically distributed. By using embedded Markov chain approach and difference operator method, queue length of the whole system at random slots and the waiting time for an arriving customer are obtained. The queue length distributions of the outside observer’s observation epoch are investigated. Numerical experiment is performed to validate the analytical results

GI/Geom/1/N/MWV queue with changeover time and searching for the optimum service rate in working vacation period

AbstractIn this paper, we consider a finite buffer size discrete-time multiple working vacation queue with changeover time. Employing the supplementary variable and embedded Markov chain techniques, we derive the steady state system length distributions at different time epochs. Based on the various system length distributions, the blocking probability, probability mass function of sojourn time and other performance measures along with some numerical examples have been discussed. Then, we use the parabolic method to search the optimum value of the service rate in working vacation period under a given cost structure

Learning a Planning Domain Model from Natural Language Process Manuals

Artificial intelligence planning techniques have been widely used in many applications. A big challenge is to automate a planning model, especially for planning applications based on natural language (NL) input. This requires the analysis and understanding of NL text and a general learning technique does not exist in real-world applications. In this article, we investigate an intelligent planning technique for natural disaster management, e.g. typhoon contingency plan generation, through natural language process manuals. A planning model is to optimise management operations when a disaster occurs in a short time. Instead of manually building the planning model, we aim to automate the planning model generation by extracting disaster management-related content through NL processing (NLP) techniques. The learning input comes from the published documents that describe the operational process of preventing potential loss in the typhoon management. We adopt a classical planning model, namely planning domain definition language (PDDL), in the typhoon contingency plan generation. We propose a novel framework of FPTCP, which stands for a Framework of Planning Typhoon Contingency Plan , for learning a domain model of PDDL from NL text. We adapt NLP techniques to construct a ternary template of sentences of NL inputs from which actions and their objects are extracted to build a domain model. We also develop a comprehensive suite of user interaction components and facilitate the involvement of users in order to improve the learned domain models. The user interaction is to remove semantic duplicates of NL objects such that the users can select model-generated actions and predicates to better fit the PDDL domain model. We detail the implementation steps of the proposed FPTCP and evaluate its performance on real-world typhoon datasets. In addition, we compare FPTCP with two state-of-the-art approaches in applications of narrative generation, and discuss its capability and limitations

Optimal design of sand blown wind tunnel

This work investigates the airflow driven by dual axial-flow fans in an atmospheric boundary layer (ABL) wind tunnel and the expected entrainment of sand movement together. The present study is conducted via 3D numerical simulation based on modelling the entire wind tunnel, including the power fan sections. Three configurations of dual fans in the tunnel are proposed. Simulation results show that the airflow in the tunnel with dual-fan configuration can satisfy the logarithmic distribution law for ABL flows. The airflow driven by the dual fans placed together at the tunnel outlet is highly similar to that in the tunnel with single fans. Although the boundary layer thickness is reduced, the maximum airflow velocity (53.393 m/s) and turbulence intensity (12.02%), which are respectively 1.75 and 1.49 times higher than those under the single-fan configuration, can be reached when dual fans are separately placed at the tunnel inlet and outlet. The simulation and experiment manifest that the separated arrangement of dual fans in the tunnel should be suitable for the experimental study of aeolian sand transport. Some measures, such as wind tunnel construction adjustment and optimal roughness element arrangement, are necessary to guarantee the required boundary layer thickness in the wind tunnel

Study on the Queue-Length Distribution in G

This paper analyzes a finite buffer size discrete-time Geo/G/1/N queue with multiple working vacations and different input rate. Using supplementary variable technique and embedded Markov chain method, the queue-length distribution solution in the form of formula at arbitrary epoch is obtained. Some performance measures associated with operating cost are also discussed based on the obtained queue-length distribution. Then, several numerical experiments follow to demonstrate the effectiveness of the obtained formulae. Finally, a state-dependent operating cost function is constructed to model an express logistics service center. Regarding the service rate during working vacation as a control variable, the optimization analysis on the cost function is carried out by using parabolic method

A value equivalence approach for solving interactive dynamic influence diagrams

Interactive dynamic influence diagrams (I-DIDs) are recognized graphical models for sequential multiagent decision making under uncertainty. They represent the problem of how a subject agent acts in a common setting shared with other agents who may act in sophisticated ways. The difficulty in solving I-DIDs is mainly due to an exponentially growing space of candidate models ascribed to other agents over time. in order to minimize the model space, the previous I-DID techniques prune behaviorally equivalent models. In this paper, we challenge the minimal set of models and propose a value equivalence approach to further compress the model space. The new method reduces the space by additionally pruning behaviourally distinct models that result in the same expected value of the subject agent’s optimal policy. To achieve this, we propose to learn the value from available data particularly in practical applications of real-time strategy games. We demonstrate the performance of the new technique in two problem domains

Toward data-driven solutions to interactive dynamic influence diagrams

With the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains

Exploiting relational tag expansion for dynamic user profile in a tag-aware ranking recommender system

A tag-aware recommender system (TRS) presents the challenge of tag sparsity in a user profile. Previous work focuses on expanding similar tags and does not link the tags with corresponding resources, therefore leading to a static user profile in the recommendation. In this article, we have proposed a new social tag expansion model (STEM) to generate a dynamic user profile to improve the recommendation performance. Instead of simply including most relevant tags, the new model focuses on the completeness of a user profile through expanding tags by exploiting their relations and includes a sufficient set of tags to alleviate the tag sparsity problem. The novel STEM-based TRS contains three operations: (1) Tag cloud generation discovers potentially relevant tags in an application domain; (2) Tag expansion finds a sufficient set of tags upon original tags; and (3) User profile refactoring builds a dynamic user profile and determines the weights of the extended tags in the profile. We analysed the STEM property in terms of recommendation accuracy and demonstrated its performance through extensive experiments over multiple datasets. The analysis and experimental results showed that the new STEM technique was able to correctly find a sufficient set of tags and to improve the recommendation accuracy by solving the tag sparsity problem. At this point, this technique has consistently outperformed state-of-art tag-aware recommendation methods in these extensive experiments