AAAI Research Articles

The Updated SERC AI and Autonomy Roadmap 2023

AbstractThe first Systems Engineering Research Center (SERC) Artificial Intelligence (AI) and Autonomy Research Roadmap was developed in 2020 and published in the first quarter 2021 special INSIGHT issue on Systems Engineering and AI. This roadmap development was heavily informed by the INCOSE Future of Systems Engineering (FuSE) initiatives. Following on in 2020, INCOSE and the SERC collaborated with the Association for Advancement of AI (AAAI) to execute two workshops entitled “AI meets Systems Engineering.” These resulted in version two of the roadmap which was published as an introductory chapter to the book “Systems Engineering and Artificial Intelligence.” In 2020 through 2023 the SERC hosted four SE4AI/AI4SE workshops with the U.S. Army that have further informed research and application at the intersection of AI and SE. This paper presents the updated version of the roadmap resulting from engagement across those four workshops. It is provided as a means to inform the SE community of the critical research needs and related applications emerging at the intersection of AI and SE.

AI climate tipping‐point discovery (ACTD)

AI climate tipping‐point discovery (ACTD)

Letter from the Editors: AI Magazine in the new era of AI

Artificial Intelligence has witnessed an exponential growth in the last decade and, thanks to its many successful and pervasive applications, it has now become a research field with profound societal impacts. The interest in AI has reached an all-time high from all sectors of our modern society, including industry, health, education and government. AI Magazine, founded in 1980, has documented the rise of AI from an elite and almost esoteric field to its current status of key player in modern society. Under the leadership of exceptional scientists with a global vision of the field, David Leake first and then Ashok Goel, it has provided a venue for vibrant discussion on technological transformations, research trends and fundamental breakthroughs.

Quantifying emotionally grounded discursive knowledge with cognitive-affective maps

Belief systems matter for all kinds of human social interaction. People have individual cognitions and feelings concerning processes in their environment, which is why they may evaluate them differently. Belief systems can be visualized with cognitive-affective maps (CAMs; as reported by Thagard (in: McGregor (ed) EMPATHICA: A computer support system with visual representations for cognitive-affective mapping, AAAI Press, CA, 2010)). However, it is unclear whether CAMs can be constructed in an intersubjective way by different researchers attempting to map the beliefs of a third party based on qualitative text data. To scrutinize this question, we combined qualitative strategies and quantitative methods of text and network analysis in a case study examining belief networks about participation. Our data set consists of 10 sets of two empirical CAMs: the first CAM was created based on participants’ freely associating concepts related to participation in education (N = 10), the second one was created based on given text data which the participants represented as a CAM following a standardized instruction manual (N = 10). Both CAM-types were compared along three dimensions of similarity (network similarity, concept association similarity, affective similarity). On all dimensions of similarity, there was substantially higher intersubjective agreement in the text-based CAMs than in the free CAMs, supporting the viability of cognitive affective mapping as an intersubjective research method for studying the emotional coherence of belief systems and discursive knowledge. In addition, this study highlights the potential for identifying group-level differences based on how participants associate concepts.

Rethinking AI magazine again

I have been affiliated with AI Magazine for a long time. In 2010, David Leake, the then Editor-in-Chief of AIM, invited me to join the magazine’s editorial board. About five years later, David and Mike Hamilton, AIM’s Managing Editor, invited me to become the magazine’s next Editor-in-Chief. After serving as an Associate Editor for about a year and receiving approval from AAAI’s Executive Council, I became the Editor-in-Chief of AI Magazine on August 1, 2016. About that time, I wrote an editorial titled Rethinking AI Magazine (Winter 2016, pp. 3-4). Now, almost five years later, as I step down as the AIM’s Editor-in-Chief, I want to revisit the vision articulated in that editorial.

2022 AAAI Squirrel AI Award Call for Nominations

2022 AAAI Squirrel AI Award Call for Nominations

The Role of Open‐Source Software in Artificial Intelligence

With this publication, we launch a new column for AI Magazine on the role of open‐source software in artificial intelligence. As the column editor, I would like to extend my welcome and invite AI Magazine readers to send short articles for future columns, which may appear in the traditional print version of AI Magazine, or on the AI Magazine interactive site currently under development. This introductory column serves to highlight my interests in open‐source software and to propose a few topics for future columns.

Application of data mining and machine learning in management accounting information system

ABSTRACT In the traditional management accounting information processing method, the method used to solve the problem is often fixed due to excessive assumptions. In order to improve its operating efficiency, combined with artificial intelligence information technology, this paper uses data mining algorithms to conduct data acquisition and rule exploration. Moreover, this paper uses statistics, machine learning and other techniques to analyze the correlation between attribute values and transform data into knowledge needed for decision-making. In addition, this paper combines machine learning algorithms to build an intelligent management accounting information system and realizes the close connection between corporate finance and business, which helps to form a closed-loop management between financial analysis, risk management, performance management, and decision-making. Finally, this paper designs experiments to verify the performance of the model. The research results show that the system constructed in this paper satisfies the intelligent demand of accounting information. REFERENCES [1] Jie Cai, Jiawei Luo, Shulin Wang, and Sheng Yang. Feature selection in machine learning: A new perspective. Neurocomputing, 300:70–79, 2018. [2] J. N. Goetz, A Brenning, H Petschko, and P. Leopold. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers and Geosciences, 81:1–11, 2015. [3] Hamid Darabi, Bahram Choubin, Omid Rahmati, Ali Torabi Haghighi, Biswajeet Pradhan, and Bjorn Klove. Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques. Journal of Hydrology, 569:142–154, 2019. [4] Alvin Rajkomar, Jeffrey Dean, and Isaac Kohane. Machine learning in medicine. England Journal of Medicine, 380(14):1347–58, apr 2019. [5] Yang Xin, Lingshuang Kong, Zhi Liu, Yuling Chen, Yanmiao Li, Hongliang Zhu, Mingcheng Gao, Haixia Hou, and Chunhua Wang. Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access, 6:35365–35381, 2018. [6] Logan Ward, Ankit Agrawal, Alok Choudhary, and Christopher Wolverton. A general-purpose machinelearning framework for predicting properties of inorganic materials. npj Computational Materials, 2, 2016. [7] Puyu Feng, Bin Wang, De Li Liu, Cathy Waters, and Qiang Yu. Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia. Agricultural and Forest Meteorology, 275:100–113, 2019. [8] Konstantina Kourou, Themis P. Exarchos, Konstantinos P. Exarchos, Michalis V. Karamouzis, and DimitriosI. Fotiadis. Machine learning applications in cancer prognosis and prediction, 2015. [9] Saleema Amershi, Maya Cakmak, W. Bradley Knox, and Todd Kulesza. Power to the people: The role of humans in interactive machine learning. AI Magazine, 35(4):105–120, 2014. [10] V Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica- Olmo, and M. Chica-Rivas. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71:804–818, 2015. [11] ConnorW Coley, Regina Barzilay, Tommi S Jaakkola, William H Green, and Klavs F Jensen. Prediction of Organic Reaction Outcomes Using Machine Learning. ACS Central Science, 3(5):434–443, may 2017. [12] Aritra Chowdhury, Elizabeth Kautz, Bulent Yener, and Daniel Lewis. Image driven machine learning methods for microstructure recognition. Computational MaterialsScience, 123:176–187, 2016.

Parameterized complexity of abduction in Schaefer’s framework

Abstract Abductive reasoning is a non-monotonic formalism stemming from the work of Peirce. It describes the process of deriving the most plausible explanations of known facts. Considering the positive version, asking for sets of variables as explanations, we study, besides the problem of wether there exists a set of explanations, two explanation size limited variants of this reasoning problem (less than or equal to, and equal to a given size bound). In this paper, we present a thorough two-dimensional classification of these problems: the first dimension is regarding the parameterized complexity under a wealth of different parameterizations, and the second dimension spans through all possible Boolean fragments of these problems in Schaefer’s constraint satisfaction framework with co-clones (T. J. Schaefer. The complexity of satisfiability problems. In Proceedings of the 10th Annual ACM Symposium on Theory of Computing, May 1–3, 1978, San Diego, California, USA, R.J. Lipton, W.A. Burkhard, W.J. Savitch, E.P. Friedman, A.V. Aho eds, pp. 216–226. ACM, 1978). Thereby, we almost complete the parameterized complexity classification program initiated by Fellows et al. (The parameterized complexity of abduction. In Proceedings of the Twenty-Sixth AAAI Conference on Articial Intelligence, July 22–26, 2012, Toronto, Ontario, Canada, J. Homann, B. Selman eds. AAAI Press, 2012), partially building on the results by Nordh and Zanuttini (What makes propositional abduction tractable. Artificial Intelligence, 172, 1245–1284, 2008). In this process, we outline a fine-grained analysis of the inherent parameterized intractability of these problems and pinpoint their FPT parts. As the standard algebraic approach is not applicable to our problems, we develop an alternative method that makes the algebraic tools partially available again.

AAAI Conferences Calendar

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.

AAAI Conferences Calendar


 
 
 This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.
 
 

AAAI Conferences Calendar

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.

AAAI Conferences Calendar


 
 
 This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.
 
 

AAAI Conferences Calendar

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.

Artificial Intelligence, Autonomy, and Human‐Machine Teams: Interdependence, Context, and Explainable AI: Editorial Introduction to the Special Articles on Context

Because in military situations, as well as for self‐driving cars, information must be processed faster than humans can achieve, determination of context computationally, also known as situational assessment, is increasingly important. In this article, we introduce the topic of context, and we discuss what is known about the heretofore intractable research problem on the effects of interdependence, present in the best of human teams; we close by proposing that interdependence must be mastered mathematically to operate human‐machine teams efficiently, to advance theory, and to make the machine actions directed by AI explainable to team members and society. The special topic articles in this issue and a subsequent issue of AI Magazine review ongoing mature research and operational programs that address context for human‐machine teams.

Letter to the Editor


 
 
 We were delighted to see No AI Is an Island: The Case for Teaming Intelligence highlighted in the latest AI Magazine. The case for teamwork is, however, not new nor as neglected as this article suggests; it is a case we and many others have made for three decades.
 
 

Decarboxylative Coupling Reaction of 2‐(1H‐Indol‐3‐yl)acetic Acids with Indole, Azaindole, Benzimidazole and Indazole Derivatives

Abstract3,3′‐Diindolylmethanes (DIMs) are an important class of indole alkaloids that exhibit anti‐inflammatory and anti‐cancer effects. Herein, we report on a new, mild and efficient copper(II)‐promoted decarboxylative coupling reaction of 2‐(1H‐indol‐3‐yl)acetic acid derivatives (1 a–h) with a variety of (substituted) indoles (2 a–t) yielding (un)symmetrically substituted DIMs (3 a–z, 3 aa–ai). Reaction of 2‐(1H‐indol‐3‐yl)acetic acid (1 a) with 7‐azaindole led to the 3,3′‐connected DIM analog 5 d, while 4‐, 5‐, and 6‐azaindoles and benzimidazole reacted at the N1‐nitrogen atom. Reaction of 1 a with 1H‐indazoles led to a mixture of 1‐ and 2‐substituted indazole derivatives. The new method allows large‐scale synthesis of biologically active DIMs.magnified image

AAAI Conferences Calendar

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Deep Models, Machine Learning, and Artificial Intelligence Applications in National and International Security — Part Two

The spring 2019 issue of AI Magazine featured three articles on deep models, machine learning, and AI applications in national and international security. In this issue, we continue the series with three additional articles, which, like the first three, examine many of the pressing issues involved in applying deep learning to the domain of security.

AAAI Conferences Calendar


 
 
 This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.