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Solution Density and Search Strategy in Narrative Generation

IEEE TRANSACTIONS ON GAMES 2022年第4期14卷 715-724页

作者： Siler, Cory Ware, Stephen G. Univ Kentucky Narrat Intelligence Lab Lexington KY 40506 USA

Intelligent interactive narrative systems coordinate a cast of nonplayer characters to make the overall story experience meaningful for the player. Narrative generation involves a tradeoff between plot-structure requirements and quality of character behavior, as well as computational efficiency. We study this tradeoff using the example of benchmark problems for narrative planning algorithms. A typical narrative planning problem calls for a sequence of actions that leads to an overall plot goal being met, while also requiring each action to respect constraints that create the appearance of character autonomy. We consider simplified solution definitions that enforce only plot requirements or only character requirements, and we measure how often each of these definitions leads to a solution that happens to meet both types of requirements-i.e., the density with which narrative plans occur among plot- or character-requirement-satisfying sequences. We then investigate whether solution densities can guide the selection of narrative planning algorithms. We compare the performance of two search strategies: one that satisfies plot requirements first and checks character requirements afterward, and one that continuously verifies character requirements. Our results show that comparing solution densities does not by itself predict which of these search strategies will be more efficient in terms of search nodes visited, suggesting that other important factors exist. We discuss what some of these factors could be. Our work opens further investigation into characterizing narrative planning algorithms and how they interact with specific domains. The results also highlight the diversity and difficulty of solving narrative planning problems.

关键词： Intelligent systems algorithm design and analysis algorithms artificial intelligence computational and artificial intelligence computer applications computers and information processing mathematics modeling systems engineering and theory

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A Probabilistic Logic of Cyber Deception

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2017年第11期12卷 2532-2544页

作者： Jajodia, Sushil Park, Noseong Pierazzi, Fabio Pugliese, Andrea Serra, Edoardo Simari, Gerardo I. Subrahmanian, V. S. George Mason Univ Fairfax VA 22030 USA Univ North Carolina Charlotte Charlotte NC 28223 USA Univ Modena & Reggio Emilia I-41121 Modena Italy Univ Calabria I-87036 Arcavacata Di Rende Italy Boise State Univ Boise ID 83725 USA Univ Nacl Sur Dept Comp Sci & Engn RA-8000 Bahia Blanca Buenos Aires Argentina Univ Nacl Sur Inst Comp Sci & Engn RA-8000 Bahia Blanca Buenos Aires Argentina Univ Maryland College Pk MD 20854 USA

Malicious attackers often scan nodes in a network in order to identify vulnerabilities that they may exploit as they traverse the network. In this paper, we propose that the system generates a mix of true and false answers in response to scan requests. If the attacker believes that all scan results are true, then he will be on a wrong path. If he believes some scan results are faked, he would have to expend time and effort in order to separate fact from fiction. We propose a probabilistic logic of deception and show that various computations are NP-hard. We model the attacker's state and show the effects of faked scan results. We then show how the defender can generate fake scan results in different states that minimize the damage the attacker can produce. We develop a Naive-PLD algorithm and a Fast-PLD heuristic algorithm for the defender to use and show experimentally that the latter performs well in a fraction of the run time of the former. We ran detailed experiments to assess the performance of these algorithms and further show that by running Fast-PLD off-line and storing the results, we can very efficiently answer run-time scan requests.

关键词： computational and artificial intelligence computer networks computer security logic-probabilistic logic network security

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Indoor Navigation With Virtual Graph Representation: Exploiting Peak Intensities of Unmodulated Luminaries

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IEEE-ACM TRANSACTIONS ON NETWORKING 2019年第1期27卷 187-200页

作者： Liu, Wenping Jiang, Hongbo Jiang, Guoyin Liu, Jiangchuan Ma, Xiaoqiang Jia, Yufu Xiao, Fu Hubei Univ Econ Sch Informat Management & Stat Wuhan 430205 Hubei Peoples R China CICCPREA Hubei Sub Ctr Wuhan 430205 Hubei Peoples R China Hunan Univ Changsha 410082 Hunan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Univ Elect Sci & Technol China Sch Publ Adm Chengdu 611731 Sichuan Peoples R China Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Hubei Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Jiangsu Peoples R China

The ubiquitous luminaries provide a new dimension for indoor navigation, as they are often well-structured and the visible light is reliable for its multipath-free nature. However, existing visible light-based technologies, which are generally frequency-based, require the modulation on light sources, modification to the device, or mounting extra devices. The combination of the cost-extensive floor map and the localization system with constraints on customized hardwares for capturing the flashing frequencies, no doubt, hinders the deployment of indoor navigation systems at scale in, nowadays, smart cities. In this paper, we provide a new perspective of indoor navigation on top of the virtual graph representation. The main idea of our proposed navigation system, named PILOT, stems from exploiting the peak intensities of ubiquitous unmodulated luminaries. In PILOT, the pedestrian paths with enriched sensory data are organically integrated to derive a meaningful graph, where each vertex corresponds to a light source and pairwise adjacent vertices (or light sources) form an edge with a computed length and direction. The graph, then, serves as a global reference frame for indoor navigation while avoiding the usage of pre-deployed floor maps, localization systems, or additional hardwares. We have implemented a prototype of PILOT on the Android platform, and extensive experiments in typical indoor environments demonstrate its effectiveness and efficiency.

关键词： Human computer interaction computational and artificial intelligence pervasive computing ubiquitous computing

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The Application of artificial intelligence Technologies as a Substitute for Reading and to Support and Enhance the Authoring of Scientific Review Articles

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IEEE ACCESS 2019年 7卷 65263-65276页

作者： Buchkremer, Ruediger Demund, Alexander Ebener, Stefan Gampfer, Fabian Jaegering, David Juergens, Andreas Klenke, Sebastian Krimpmann, Dominik Schmank, Jasmin Spiekermann, Markus Wahlers, Michael Wiepke, Markus FOM Univ Appl Sci Inst IT Management & Digitizat D-40211 Dusseldorf Germany

To gain a comprehensive overview of new scientific findings with the enormous, ever-increasing amount of published information, we apply a new combinatorial approach that complements the process of reading scientific articles by supplementing artificial intelligence technologies. We present a combinatorial approach, which we illustrate in the form of a "double funnel of artificial intelligence." Our approach suggests to largely increase the amount of data at the beginning of the data collection process and to subsequently clean and enrich the data set in order to gain much more knowledge at the end of the procedure compared to a "classical" literature review. We use natural language processing and text visualization techniques to uncover findings that are generally unbeknown to the human reader due to the inability to process very large amounts of text. By illustrating the individual steps using practical examples taken from use cases, we demonstrate the merits of our approach. With our methodology, we are able to reproduce findings from "regular" review papers;however, we discover additional and new findings in different fields, such as data science or medicine. We also point out the limitations of our approach. Finally, we make suggestions as to how the methodology could be further developed.

关键词： computational and artificial intelligence document handling fuzzy control knowledge acquisition pattern analysis scientific publishing text mining text processing

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Exploring Energy and Accuracy Tradeoff in Structure Simplification of Trained Deep Neural Networks

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2018年第4期8卷 836-848页

作者： Zhang, Boyu Davoodi, Azadeh Hu, Yu Hen Univ Wisconsin Elect & Comp Engn Dept Madison WI 53706 USA

This paper presents a structure simplification procedure that allows efficient energy and accuracy tradeoffs in implementation of trained deep neural networks (DNNs). This structure simplification procedure identifies and eliminates redundant neurons in any layer of the DNN based on the output values of these neurons over the training samples. This procedure may be applied to all layers of a DNN. Pareto-optimal points in terms of accuracy and energy consumption can be easily identified from the configurations across different layers. After redundant neurons are discarded, the weights connect to the remaining neurons will be updated using matrix multiplication without retraining. Yet, retraining may still be applied if desired to further fine tune the performance. In our experiments, we show energy-accuracy tradeoff that provides clear guidance to achieve efficient realization of trained DNNs. We also observe significant implementation cost reductions with up to 11.50x in energy and 12.30x in memory while the relative performance (accuracy) loss is negligible. We also discuss and validate strategies to apply our procedure to multiple layers in a DNN in order to maximize the compression ratio of the network after simplification subject to an overall budget in degradation of accuracy across all the simplified layers.

关键词： computational and artificial intelligence neural networks feedforward neural networks multi-layer neural network

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ComFlex: Composable and Flexible Resource Management for the IoT

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IEEE INTERNET OF THINGS JOURNAL 2021年第22期8卷 16406-16417页

作者： Lee, Gyeongmin Kim, Bongjun Song, Seungbin Heo, Seonyeong Kim, Hanjun POSTECH Dept Comp Sci & Engn Pohang 37673 South Korea Yonsei Univ Dept Elect & Elect Engn Seoul 03722 South Korea

The Internet of Things (IoT) enables new services, such as smart home and smart healthcare integrating various resources of networked devices. However, managing IoT resources is very challenging because a device may have multiple different resources and the service accesses subsets of the resources in multiple devices. To simplify the resource management, the existing IoT frameworks provide resource models and protocols for manufacturers and programmers, but their access granularity is too coarse grained causing overprivilege problems, or too fine grained causing management overheads. To avoid the overprivilege problems without the management overheads, this work proposes a new composable and flexible resource management scheme and implements its prototype compiler-runtime framework called ComFlex. The ComFlex compiler allows manufacturers to register their devices as composition of fine-grained resources by inheriting the existing interfaces of the resources, and programmers to define their own access granularity as a virtual resource that consists of the fine-grained resources of multiple devices. The ComFlex runtime supports fine-grained access control without additional overheads by mapping the fine-grained resources of the virtual resource into physical resources of different devices. To evaluate the ComFlex framework, this work implements 52 resources on 14 devices and six IoT services with ComFlex and an existing fine-grained resource management scheme. Compared to the existing scheme, the ComFlex framework supports the IoT services with 40.2% fewer lines of code and 80.9% less discovery time, without any overprivilege problem and response time delay.

关键词： Access control Resource management Registers Internet of Things Programming Runtime C plus plus languages Communication systems communications technology computational and artificial intelligence Internet of Things (IoT) Internet programming environments software engineering

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Adaptive Named Entity Recognition Using Distant Supervision for Contemporary Written Texts

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IEEE ACCESS 2021年 9卷 80405-80414页

作者： Kim, Juae Kim, Yejin Kang, Sangwoo Seo, Jungyun AIRS Co Hyundai Motor Grp Seoul 06134 South Korea LG Elect Artificial Intelligence Lab Seoul 06763 South Korea Gachon Univ Sch Comp Seungnam 13120 South Korea Sogang Univ Dept Comp Engn Seoul 04107 South Korea

Named entity recognition (NER) is the process of categorizing named entities in a given text that suffers from the lack of labeled corpora, which is a long-standing issue. Deep neural networks have been successfully applied to NER tasks. However, they require a large number of annotated data. Regardless of the number of data made available, annotation requires significant human effort, which is expensive and time-consuming. Moreover, collecting labeled data that reflect contemporary surrounding statuses requires exhaustive follow-up and incurs correspondingly higher costs. Current NERs typically focus on the supervised learning of hand-crafted data. The most well-known dataset for NER shared tasks, which was released at the 2003 Conference on Natural Language Learning, is used for basic training and evaluation. Although the data are qualified, the database has low coverage of timely material. In this paper, we illustrate methods for swiftly labeling up-to-date data via distant supervision. To tackle the difficulty of annotating contemporary written texts, we generate labeled data articles that reflect the latest issues. We evaluated the proposed methods with bidirectional long short-term memory conditional random-field architecture using static and contextualized embedding methods. Our proposed models perform higher than state-of-the-art methods with average F1-scores 3.09% better with weakly labeled Wikipedia data and 3.47% better with Cable News Network data. When using the NER model with Flair embedding, our method shows 1.50 and 3.26% higher F1-scores with weakly labeled Wikipedia and news data, respectively. Qualitatively, the proposed model also performs better when extracting contemporary keywords.

关键词： Training Transfer learning Internet Information services Encyclopedias Electronic publishing Task analysis computational and artificial intelligence named entity recognition natural language processing neural networks transfer learning weakly supervised learning

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DeepCaustics: Classification and Removal of Caustics From Underwater Imagery

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IEEE JOURNAL OF OCEANIC ENGINEERING 2019年第3期44卷 728-738页

作者： Forbes, Timothy Goldsmith, Mark Mudur, Sudhir Poullis, Charalambos Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

Caustics are complex physical phenomena resulting from the projection of light rays being reflected or refracted by a curved surface. In this paper, we address the problem of classifying and removing caustics from images and propose a novel solution based on two convolutional neural networks: SalienceNet and DeepCaustics. Caustics result in changes in illumination that are continuous in nature;therefore, the first network is trained to produce a classification of caustics that is represented as a saliency map of the likelihood of caustics occurring at a pixel. In applications where caustic removal is essential, the second network is trained to generate a caustic-free image. It is extremely hard to generate real ground truth for caustics. We demonstrate how synthetic caustic data can be used for training in such cases, and then transfer the learning to real data. To the best of our knowledge, out of the handful of techniques that have been proposed, this is the first time that the complex problem of caustic removal has been reformulated and addressed as a classification and learning problem. This paper is motivated by the real-world challenges in underwater archaeology.

关键词： computational and artificial intelligence image denoising image enhancement image processing

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Hierarchical Multiagent Reinforcement Learning for Allocating Guaranteed Display Ads

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第10期33卷 5361-5373页

作者： Wang, Lu Han, Lei Chen, Xinru Li, Chengchang Huang, Junzhou Zhang, Weinan Zhang, Wei He, Xiaofeng Luo, Dijun East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Tencent AI Lab Bellevue WA 98004 USA East China Normal Univ Sch Software Engn Shanghai 200062 Peoples R China Shanghai Key Lab Trustworthy Comp Shanghai 200062 Peoples R China Tencent Inc Tencent AI Lab Shenzhen 518057 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

In this article, we study the problem of guaranteed display ads (GDAs) allocation, which requires proactively allocate display ads to different impressions to fulfill their impression demands indicated in the contracts. Existing methods for this problem either assume the impressions that are static or solely consider a specific ad's benefits. Thus, it is hard to generalize to the industrial production scenario where the impressions are dynamical and large-scale, and the overall allocation optimality of all the considered GDAs is required. To bridge this gap, we formulate this problem as a sequential decision-making problem in the scope of multiagent reinforcement learning (MARL), by assigning an allocation agent to each ad and coordinating all the agents for allocating GDAs. The inputs are the states (e.g., the demands of the ad and the remaining time steps for displaying the ads) of each ad and the impressions at different time steps, and the outputs are the display ratios of each ad for each impression. Specifically, we propose a novel hierarchical MARL (HMARL) method that creates hierarchies over the agent policies to handle a large number of ads and the dynamics of impressions. HMARL contains: 1) a manager policy to navigate the agent to choose an appropriate subpolicy and 2) a set of subpolicies that let the agents perform diverse conditioning on their states. Extensive experiments on three real-world data sets from the Tencent advertising platform with tens of millions of records demonstrate significant improvements of HMARL over state-of-the-art approaches.

关键词： Resource management Contracts Advertising Statistics Sociology Reinforcement learning Real-time systems artificial intelligence computational and artificial intelligence decision support systems

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Diacritizing Arabic Text Using a Single Hidden Markov Model

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IEEE ACCESS 2018年 6卷 36522-36529页

作者： Khorsheed, Mohammad S. Univ Cambridge Comp Lab Cambridge CB3 0FD England King Abdulaziz City Sci & Technol Natl Ctr Robot & Intelligent Syst Riyadh 11442 Saudi Arabia

The main obstacle in the development of a robust Arabic text-to-speech system is the ability to place diacritics, that is, small marks above and below the raw text that indicate the correct pronunciation. In this paper, we propose a system that can retrieve the diacritics that match a given Arabic text. First, the system injects the raw text into an engine that is based on a single hidden Markov model. The engine then generates an optimal path through its states. The system finally matches the state sequence with its equivalent diacritics and sets them in place within the text. Experiential results on diverse data sets demonstrate the robustness of the proposed system, even with samples that are novel to the system.

关键词： computational and artificial intelligence hidden Markov models machine intelligence pattern analysis text processing

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