Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201

Artificial Superintelligence: Coordination & Strategy

Attention in the AI safety community has increasingly started to include strategic considerations of coordination between relevant actors in the field of AI and AI safety, in addition to the steadily growing work on the technical considerations of building safe AI systems. This shift has several reasons: Multiplier effects, pragmatism, and urgency. Given the benefits of coordination between those working towards safe superintelligence, this book surveys promising research in this emerging field regarding AI safety. On a meta-level, the hope is that this book can serve as a map to inform those working in the field of AI coordination about other promising efforts. While this book focuses on AI safety coordination, coordination is important to most other known existential risks (e.g., biotechnology risks), and future, human-made existential risks. Thus, while most coordination strategies in this book are specific to superintelligence, we hope that some insights yield “collateral benefits” for the reduction of other existential risks, by creating an overall civilizational framework that increases robustness, resiliency, and antifragility

General intelligence requires rethinking exploration

We are at the cusp of a transition from 'learning from data' to 'learning what data to learn from' as a central focus of artificial intelligence (AI) research. While the first-order learning problem is not completely solved, large models under unified architectures, such as transformers, have shifted the learning bottleneck from how to effectively train models to how to effectively acquire and use task-relevant data. This problem, which we frame as exploration, is a universal aspect of learning in open-ended domains like the real world. Although the study of exploration in AI is largely limited to the field of reinforcement learning, we argue that exploration is essential to all learning systems, including supervised learning. We propose the problem of generalized exploration to conceptually unify exploration-driven learning between supervised learning and reinforcement learning, allowing us to highlight key similarities across learning settings and open research challenges. Importantly, generalized exploration is a necessary objective for maintaining open-ended learning processes, which in continually learning to discover and solve new problems, provides a promising path to more general intelligence

An Abstract Framework for Non-Cooperative Multi-Agent Planning

[EN] In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents¿ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.This work was partially funded by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by Universitat Politecnica de Valencia (UPV) PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana Fondo Social Europeo.Jordán, J.; Bajo, J.; Botti, V.; Julian Inglada, VJ. (2019). An Abstract Framework for Non-Cooperative Multi-Agent Planning. Applied Sciences. 9(23):1-18. https://doi.org/10.3390/app9235180S118923De Weerdt, M., & Clement, B. (2009). Introduction to planning in multiagent systems. Multiagent and Grid Systems, 5(4), 345-355. doi:10.3233/mgs-2009-0133Dunne, P. E., Kraus, S., Manisterski, E., & Wooldridge, M. (2010). Solving coalitional resource games. Artificial Intelligence, 174(1), 20-50. doi:10.1016/j.artint.2009.09.005Torreño, A., Onaindia, E., Komenda, A., & Štolba, M. (2018). Cooperative Multi-Agent Planning. ACM Computing Surveys, 50(6), 1-32. doi:10.1145/3128584Fikes, R. E., & Nilsson, N. J. (1971). Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3-4), 189-208. doi:10.1016/0004-3702(71)90010-5Hoffmann, J., & Nebel, B. (2001). The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research, 14, 253-302. doi:10.1613/jair.855Dukeman, A., & Adams, J. A. (2017). Hybrid mission planning with coalition formation. Autonomous Agents and Multi-Agent Systems, 31(6), 1424-1466. doi:10.1007/s10458-017-9367-7Hadad, M., Kraus, S., Ben-Arroyo Hartman, I., & Rosenfeld, A. (2013). Group planning with time constraints. Annals of Mathematics and Artificial Intelligence, 69(3), 243-291. doi:10.1007/s10472-013-9363-9Guo, Y., Pan, Q., Sun, Q., Zhao, C., Wang, D., & Feng, M. (2019). Cooperative Game-based Multi-Agent Path Planning with Obstacle Avoidance*. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). doi:10.1109/isie.2019.8781205v. Neumann, J. (1928). Zur Theorie der Gesellschaftsspiele. Mathematische Annalen, 100(1), 295-320. doi:10.1007/bf01448847Mookherjee, D., & Sopher, B. (1994). Learning Behavior in an Experimental Matching Pennies Game. Games and Economic Behavior, 7(1), 62-91. doi:10.1006/game.1994.1037Ochs, J. (1995). Games with Unique, Mixed Strategy Equilibria: An Experimental Study. Games and Economic Behavior, 10(1), 202-217. doi:10.1006/game.1995.1030Applegate, C., Elsaesser, C., & Sanborn, J. (1990). An architecture for adversarial planning. IEEE Transactions on Systems, Man, and Cybernetics, 20(1), 186-194. doi:10.1109/21.47820Sailer, F., Buro, M., & Lanctot, M. (2007). Adversarial Planning Through Strategy Simulation. 2007 IEEE Symposium on Computational Intelligence and Games. doi:10.1109/cig.2007.368082Willmott, S., Richardson, J., Bundy, A., & Levine, J. (2001). Applying adversarial planning techniques to Go. Theoretical Computer Science, 252(1-2), 45-82. doi:10.1016/s0304-3975(00)00076-1Nau, D. S., Au, T. C., Ilghami, O., Kuter, U., Murdock, J. W., Wu, D., & Yaman, F. (2003). SHOP2: An HTN Planning System. Journal of Artificial Intelligence Research, 20, 379-404. doi:10.1613/jair.1141Knuth, D. E., & Moore, R. W. (1975). An analysis of alpha-beta pruning. Artificial Intelligence, 6(4), 293-326. doi:10.1016/0004-3702(75)90019-3Vickrey, W. (1961). COUNTERSPECULATION, AUCTIONS, AND COMPETITIVE SEALED TENDERS. The Journal of Finance, 16(1), 8-37. doi:10.1111/j.1540-6261.1961.tb02789.xClarke, E. H. (1971). Multipart pricing of public goods. Public Choice, 11(1), 17-33. doi:10.1007/bf01726210Groves, T. (1973). Incentives in Teams. Econometrica, 41(4), 617. doi:10.2307/1914085Savaux, J., Vion, J., Piechowiak, S., Mandiau, R., Matsui, T., Hirayama, K., … Silaghi, M. (2016). DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents. 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). doi:10.1109/wi.2016.0057Buzing, P., Mors, A. ter, Valk, J., & Witteveen, C. (2006). Coordinating Self-interested Planning Agents. Autonomous Agents and Multi-Agent Systems, 12(2), 199-218. doi:10.1007/s10458-005-6104-4Ter Mors, A., & Witteveen, C. (s. f.). Coordinating Non Cooperative Planning Agents: Complexity Results. IEEE/WIC/ACM International Conference on Intelligent Agent Technology. doi:10.1109/iat.2005.60Hrnčíř, J., Rovatsos, M., & Jakob, M. (2015). Ridesharing on Timetabled Transport Services: A Multiagent Planning Approach. Journal of Intelligent Transportation Systems, 19(1), 89-105. doi:10.1080/15472450.2014.941759Galuszka, A., & Swierniak, A. (2009). Planning in Multi-agent Environment Using Strips Representation and Non-cooperative Equilibrium Strategy. Journal of Intelligent and Robotic Systems, 58(3-4), 239-251. doi:10.1007/s10846-009-9364-4Rosenthal, R. W. (1973). A class of games possessing pure-strategy Nash equilibria. International Journal of Game Theory, 2(1), 65-67. doi:10.1007/bf01737559Jordán, J., Torreño, A., de Weerdt, M., & Onaindia, E. (2017). A better-response strategy for self-interested planning agents. Applied Intelligence, 48(4), 1020-1040. doi:10.1007/s10489-017-1046-5Veloso, M., Muñoz-Avila, H., & Bergmann, R. (1996). Case-based planning: selected methods and systems. AI Communications, 9(3), 128-137. doi:10.3233/aic-1996-9305VOORNEVELD, M., BORM, P., VAN MEGEN, F., TIJS, S., & FACCHINI, G. (1999). CONGESTION GAMES AND POTENTIALS RECONSIDERED. International Game Theory Review, 01(03n04), 283-299. doi:10.1142/s0219198999000219Han-Lim Choi, Brunet, L., & How, J. P. (2009). Consensus-Based Decentralized Auctions for Robust Task Allocation. IEEE Transactions on Robotics, 25(4), 912-926. doi:10.1109/tro.2009.2022423Monderer, D., & Shapley, L. S. (1996). Potential Games. Games and Economic Behavior, 14(1), 124-143. doi:10.1006/game.1996.0044Friedman, J. W., & Mezzetti, C. (2001). Learning in Games by Random Sampling. Journal of Economic Theory, 98(1), 55-84. doi:10.1006/jeth.2000.2694Aamodt, A., & Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(1), 39-59. doi:10.3233/aic-1994-7104Bertsekas, D. P. (1988). The auction algorithm: A distributed relaxation method for the assignment problem. Annals of Operations Research, 14(1), 105-123. doi:10.1007/bf02186476Bertsekas, D. P., & Castanon, D. A. (1989). The auction algorithm for the transportation problem. Annals of Operations Research, 20(1), 67-96. doi:10.1007/bf0221692

Designing Fiduciary Artificial Intelligence

A fiduciary is a trusted agent that has the legal duty to act with loyalty and care towards a principal that employs them. When fiduciary organizations interact with users through a digital interface, or otherwise automate their operations with artificial intelligence, they will need to design these AI systems to be compliant with their duties. This article synthesizes recent work in computer science and law to develop a procedure for designing and auditing Fiduciary AI. The designer of a Fiduciary AI should understand the context of the system, identify its principals, and assess the best interests of those principals. Then the designer must be loyal with respect to those interests, and careful in an contextually appropriate way. We connect the steps in this procedure to dimensions of Trustworthy AI, such as privacy and alignment. Fiduciary AI is a promising means to address the incompleteness of data subject's consent when interacting with complex technical systems

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

Through the Lens of Core Competency: Survey on Evaluation of Large Language Models

From pre-trained language model (PLM) to large language model (LLM), the field of natural language processing (NLP) has witnessed steep performance gains and wide practical uses. The evaluation of a research field guides its direction of improvement. However, LLMs are extremely hard to thoroughly evaluate for two reasons. First of all, traditional NLP tasks become inadequate due to the excellent performance of LLM. Secondly, existing evaluation tasks are difficult to keep up with the wide range of applications in real-world scenarios. To tackle these problems, existing works proposed various benchmarks to better evaluate LLMs. To clarify the numerous evaluation tasks in both academia and industry, we investigate multiple papers concerning LLM evaluations. We summarize 4 core competencies of LLM, including reasoning, knowledge, reliability, and safety. For every competency, we introduce its definition, corresponding benchmarks, and metrics. Under this competency architecture, similar tasks are combined to reflect corresponding ability, while new tasks can also be easily added into the system. Finally, we give our suggestions on the future direction of LLM's evaluation

The Road to General Intelligence

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book