Reports of the AAAI 2014 Conference Workshops

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities — Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.

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Reports on the 2015 AAAI Workshop Program

AI Magazine ◽

10.1609/aimag.v36i2.2590 ◽

2015 ◽

Vol 36 (2) ◽

pp. 90-101

Author(s):

Stefano V. Albrecht ◽

J. Christopher Beck ◽

David L. Buckeridge ◽

Adi Botea ◽

Cornelia Caragea ◽

...

Keyword(s):

Artificial Intelligence ◽

Imperfect Information ◽

Autonomous Robots ◽

Turing Test ◽

Assistive Technologies ◽

General Competency ◽

Computational Sustainability ◽

Wide Range ◽

Algorithm Configuration ◽

And Behavior

AAAI's 2015 Workshop Program was held Sunday and Monday, January 25–26, 2015 at the Hyatt Regency Austin Hotel in Austion, Texas, USA. The AAAI-15 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included AI and Ethics, AI for Cities, AI for Transportation: Advice, Interactivity and Actor Modeling, Algorithm Configuration, Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Beyond the Turing Test, Computational Sustainability, Computer Poker and Imperfect Information, Incentive and Trust in E-Communities, Multiagent Interaction without Prior Coordination, Planning, Search, and Optimization, Scholarly Big Data: AI Perspectives, Challenges, and Ideas, Trajectory-Based Behaviour Analytics, World Wide Web and Public Health Intelligence, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, and Learning for General Competency in Video Games.

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Reports of the Workshops of the 32nd AAAI Conference on Artificial Intelligence

AI Magazine ◽

10.1609/aimag.v39i4.2823 ◽

2018 ◽

Vol 39 (4) ◽

pp. 45-56

Author(s):

Bruno Bouchard ◽

Kevin Bouchard ◽

Noam Brown ◽

Niyati Chhaya ◽

Eitan Farchi ◽

...

Keyword(s):

Artificial Intelligence ◽

New Orleans ◽

Cyber Security ◽

Imperfect Information ◽

Assistive Technologies ◽

Intent Recognition ◽

Affective Content ◽

Wide Range ◽

Affective Content Analysis ◽

Declarative Learning

The AAAI-18 workshop program included 15 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 2–7, 2018, at the Hilton New Orleans Riverside in New Orleans, Louisiana, USA. This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the Artificial Intelligence for Cyber Security workshop; the AI for Imperfect-Information Games; the Declarative Learning Based Programming workshop; the Engineering Dependable and Secure Machine Learning Systems workshop; the Health Intelligence workshop; the Knowledge Extraction from Games workshop; the Plan, Activity, and Intent Recognition workshop; the Planning and Inference workshop; the Preference Handling workshop; the Reasoning and Learning for Human-Machine Dialogues workshop; and the the AI Enhanced Internet of Things Data Processing for Intelligent Applications workshop.

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The AAAI-13 Conference Workshops

AI Magazine ◽

10.1609/aimag.v34i4.2511 ◽

2013 ◽

Vol 34 (4) ◽

pp. 108-115

Author(s):

Vikas Agrawal ◽

Christopher Archibald ◽

Mehul Bhatt ◽

Hung Bui ◽

Diane J. Cook ◽

...

Keyword(s):

Artificial Intelligence ◽

Health Informatics ◽

Imperfect Information ◽

Intent Recognition ◽

System Architectures ◽

Wide Range ◽

Trading Agent ◽

Context Aware System ◽

Plan Activity ◽

And Robotics

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14‚Äì15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16).

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Reports of the 2016 AAAI Workshop Program

AI Magazine ◽

10.1609/aimag.v37i3.2680 ◽

2016 ◽

Vol 37 (3) ◽

pp. 99-108

Author(s):

Stefano Albrecht ◽

Bruno Bouchard ◽

John S. Brownstein ◽

David L. Buckeridge ◽

Cornelia Caragea ◽

...

Keyword(s):

Artificial Intelligence ◽

Cyber Security ◽

Health Informatics ◽

Smart Grids ◽

Imperfect Information ◽

Assistive Technologies ◽

Exploratory Research ◽

Ai Ethics ◽

Informal Setting ◽

Declarative Learning

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) was held at the beginning of the conference, February 12-13, 2016. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers and users on topics of current interest. To foster interaction and exchange of ideas, the workshops were kept small, with 25-65 participants. Attendance was sometimes limited to active participants only, but most workshops also allowed general registration by other interested individuals. The AAAI-16 Workshops were an excellent forum for exploring emerging approaches and task areas, for bridging the gaps between AI and other fields or between subfields of AI, for elucidating the results of exploratory research, or for critiquing existing approaches. The fifteen workshops held at AAAI-16 were Artificial Intelligence Applied to Assistive Technologies and Smart Environments (WS-16-01), AI, Ethics, and Society (WS-16-02), Artificial Intelligence for Cyber Security (WS-16-03), Artificial Intelligence for Smart Grids and Smart Buildings (WS-16-04), Beyond NP (WS-16-05), Computer Poker and Imperfect Information Games (WS-16-06), Declarative Learning Based Programming (WS-16-07), Expanding the Boundaries of Health Informatics Using AI (WS-16-08), Incentives and Trust in Electronic Communities (WS-16-09), Knowledge Extraction from Text (WS-16-10), Multiagent Interaction without Prior Coordination (WS-16-11), Planning for Hybrid Systems (WS-16-12), Scholarly Big Data: AI Perspectives, Challenges, and Ideas (WS-16-13), Symbiotic Cognitive Systems (WS-16-14), and World Wide Web and Population Health Intelligence (WS-16-15).

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Quantum Go Machine

10.21203/rs.3.rs-94303/v1 ◽

2020 ◽

Author(s):

Lu-Feng Qiao ◽

Jun Gao ◽

Zhi-Qiang Jiao ◽

Zhe-Yong Zhang ◽

Zhu Cao ◽

...

Keyword(s):

Artificial Intelligence ◽

Time Series ◽

Imperfect Information ◽

Quantum Physics ◽

Time Series Data ◽

Single Point ◽

Series Data ◽

Polarization Degree ◽

Classical Counterpart ◽

Wide Range

Abstract Go has long been considered as a testbed for artificial intelligence. By introducing certain quantum features, such as superposition and collapse of wavefunction, we experimentally demonstrate a quantum version of Go by using correlated photon pairs entangled in polarization degree of freedom. The total dimension of Hilbert space of the generated states grows exponentially as two players take turns to place the stones in time series. As nondeterministic and imperfect information games are more difficult to solve using nowadays technology, we excitedly find that the inherent randomness in quantum physics can bring the game nondeterministic trait, which does not exist in the classical counterpart. Some quantum resources, like coherence or entanglement, can also be encoded to represent the state of quantum stones. Adjusting the quantum resource may vary the average imperfect information (as comparison classical Go is a perfect information game) of a single game. We further verify its non-deterministic feature by showing the unpredictability of the time series data obtained from different classes of quantum state. Finally, by comparing quantum Go with a few typical games that are widely studied in artificial intelligence, we find that quantum Go can cover a wide range of game difficulties rather than a single point. Our results establish a paradigm of inventing new games with quantum-enabled difficulties by harnessing inherent quantum features and resources, and provide a versatile platform for the test of new algorithms to both classical and quantum machine learning.

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Representations, Affordances, and Interactive Systems

Multimodal Technologies and Interaction ◽

10.3390/mti5050023 ◽

2021 ◽

Vol 5 (5) ◽

pp. 23

Author(s):

Robert Rowe

Keyword(s):

Artificial Intelligence ◽

Cognitive Science ◽

Data Structures ◽

Digital Computer ◽

Mental Representations ◽

Interactive Systems ◽

Algorithmic Composition ◽

The World ◽

History Of

The history of algorithmic composition using a digital computer has undergone many representations—data structures that encode some aspects of the outside world, or processes and entities within the program itself. Parallel histories in cognitive science and artificial intelligence have (of necessity) confronted their own notions of representations, including the ecological perception view of J.J. Gibson, who claims that mental representations are redundant to the affordances apparent in the world, its objects, and their relations. This review tracks these parallel histories and how the orientations and designs of multimodal interactive systems give rise to their own affordances: the representations and models used expose parameters and controls to a creator that determine how a system can be used and, thus, what it can mean.

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Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

Future Internet ◽

10.3390/fi13050124 ◽

2021 ◽

Vol 13 (5) ◽

pp. 124

Author(s):

Jiseong Son ◽

Chul-Su Lim ◽

Hyoung-Seop Shim ◽

Ji-Sun Kang

Keyword(s):

Artificial Intelligence ◽

Domain Knowledge ◽

Open Data ◽

Heterogeneous Data ◽

Big Data Analysis ◽

Knowledge Graph ◽

Cross Domain ◽

Disaster Data ◽

Knowledge Graphs ◽

Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

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Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽

10.3390/info12070275 ◽

2021 ◽

Vol 12 (7) ◽

pp. 275

Author(s):

Peter Cihon ◽

Jonas Schuett ◽

Seth D. Baum

Keyword(s):

Artificial Intelligence ◽

Corporate Governance ◽

Law Enforcement ◽

Nonprofit Organizations ◽

Public Interest ◽

Department Of Defense ◽

The Public ◽

Wide Range ◽

The Media ◽

The U.S

Corporations play a major role in artificial intelligence (AI) research, development, and deployment, with profound consequences for society. This paper surveys opportunities to improve how corporations govern their AI activities so as to better advance the public interest. The paper focuses on the roles of and opportunities for a wide range of actors inside the corporation—managers, workers, and investors—and outside the corporation—corporate partners and competitors, industry consortia, nonprofit organizations, the public, the media, and governments. Whereas prior work on multistakeholder AI governance has proposed dedicated institutions to bring together diverse actors and stakeholders, this paper explores the opportunities they have even in the absence of dedicated multistakeholder institutions. The paper illustrates these opportunities with many cases, including the participation of Google in the U.S. Department of Defense Project Maven; the publication of potentially harmful AI research by OpenAI, with input from the Partnership on AI; and the sale of facial recognition technology to law enforcement by corporations including Amazon, IBM, and Microsoft. These and other cases demonstrate the wide range of mechanisms to advance AI corporate governance in the public interest, especially when diverse actors work together.

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The Research and Application of Artificial Intelligence in the Field of Intelligent Robot

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.898.763 ◽

2014 ◽

Vol 898 ◽

pp. 763-766

Author(s):

Zhi Hao Li

Keyword(s):

Artificial Intelligence ◽

Environmental Changes ◽

Vision System ◽

Mineral Exploration ◽

Intelligent Robot ◽

Application Range ◽

Intelligent Robots ◽

External Data ◽

Wide Range ◽

Artificial Intelligence Application

The research and application of artificial intelligence has a very wide range in intelligent robot field. Intelligent robot can not only make use of artificial intelligence gain access to external data, information, (such as stereo vision system, face recognition and tracking, etc.), and then deal with it so as to exactly describe external environment, and complete a task independently, owing the ability of learning knowledge, but also have self-many kinds of artificial intelligence like judgment and decision making, processing capacity and so on. It can make corresponding decision according to environmental changes. Its application range is expanding. In deep sea exploration, star exploration, mineral exploration, heavy pollution, domestic service, entertainment clubs, health care and so on, the figure of intelligent robots artificial intelligence application can all be seen.

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APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN MS. PAC-MAN GAME: A REVIEW

International Journal of Creative Future and Heritage (TENIAT) ◽

10.47252/teniat.v3i1.251 ◽

2015 ◽

Vol 3 (1) ◽

Author(s):

Tse Guan Tan ◽

Jason Teo

Keyword(s):

Artificial Intelligence ◽

Content Analysis ◽

Video Games ◽

Analysis Method ◽

Engineering Economics ◽

Game Ai ◽

Content Analysis Method ◽

Wide Range ◽

Computer Based ◽

Future Direction

AbstrakTeknik Kecerdasan Buatan (AI) berjaya digunakan dan diaplikasikan dalam pelbagai bidang, termasukpembuatan, kejuruteraan, ekonomi, perubatan dan ketenteraan. Kebelakangan ini, terdapat minat yangsemakin meningkat dalam Permainan Kecerdasan Buatan atau permainan AI. Permainan AI merujukkepada teknik yang diaplikasikan dalam permainan komputer dan video seperti pembelajaran, pathfinding,perancangan, dan lain-lain bagi mewujudkan tingkah laku pintar dan autonomi kepada karakter dalampermainan. Objektif utama kajian ini adalah untuk mengemukakan beberapa teknik yang biasa digunakandalam merekabentuk dan mengawal karakter berasaskan komputer untuk permainan Ms Pac-Man antaratahun 2005-2012. Ms Pac-Man adalah salah satu permainan yang digunakan dalam siri pertandinganpermainan diperingkat antarabangsa sebagai penanda aras untuk perbandingan pengawal autonomi.Kaedah analisis kandungan yang menyeluruh dijalankan secara ulasan dan sorotan literatur secara kritikal.Dapatan kajian menunjukkan bahawa, walaupun terdapat berbagai teknik, limitasi utama dalam kajianterdahulu untuk mewujudkan karakter permaianan Pac Man adalah kekurangan Generalization Capabilitydalam kepelbagaian karakter permainan. Hasil kajian ini akan dapat digunakan oleh penyelidik untukmeningkatkan keupayaan Generalization AI karakter permainan dalam Pasaran Permainan KecerdasanBuatan. Abstract Artificial Intelligence (AI) techniques are successfully used and applied in a wide range of areas, includingmanufacturing, engineering, economics, medicine and military. In recent years, there has been anincreasing interest in Game Artificial Intelligence or Game AI. Game AI refers to techniques applied incomputer and video games such as learning, pathfinding, planning, and many others for creating intelligentand autonomous behaviour to the characters in games. The main objective of this paper is to highlightseveral most common of the AI techniques for designing and controlling the computer-based charactersto play Ms. Pac-Man game between years 2005-2012. The Ms. Pac-Man is one of the games that used asbenchmark for comparison of autonomous controllers in a series of international Game AI competitions.An extensive content analysis method was conducted through critical review on previous literature relatedto the field. Findings highlight, although there was various and unique techniques available, the majorlimitation of previous studies for creating the Ms. Pac-Man game characters is a lack of generalizationcapability across different game characters. The findings could provide the future direction for researchersto improve the Generalization A.I capability of game characters in the Game Artificial Intelligence market.

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