Trapped in the Greenhouse?: Regulating Carbon Dioxide after FDA v. Brown & Williamson Tobacco Corp.

An architecture for actionbased planning and cooperation between multiple AI-agents based on the GOAP-architecture was developed together with a system to be used in advanced AI-courses at Linköping unversity. The architecture was implemented in this system to show the possibilities of our work

Comparing humans and AI agents

Comparing humans and machines is one important source of information about both machine and human strengths and limitations. Most of these comparisons and competitions are performed in rather specific tasks such as calculus, speech recognition, translation, games, etc. The information conveyed by these experiments is limited, since it portrays that machines are much better than humans at some domains and worse at others. In fact, CAPTCHAs exploit this fact. However, there have only been a few proposals of general intelligence tests in the last two decades, and, to our knowledge, just a couple of implementations and evaluations. In this paper, we implement one of the most recent test proposals, devise an interface for humans and use it to compare the intelligence of humans and Q-learning, a popular reinforcement learning algorithm. The results are highly informative in many ways, raising many questions on the use of a (universal) distribution of environments, on the role of measuring knowledge acquisition, and other issues, such as speed, duration of the test, scalability, etc.We thank the anonymous reviewers for their helpful comments. We also thank José Antonio Martín H. for helping us with several issues about the RL competition, RL-Glue and reinforcement learning in general. We are also grateful to all the subjects who took the test. We also thank the funding from the Spanish MEC and MICINN for projects TIN2009-06078- E/TIN, Consolider-Ingenio CSD2007-00022 and TIN2010-21062-C02, for MEC FPU grant AP2006-02323, and Generalitat Valenciana for Prometeo/2008/051Insa Cabrera, J.; Dowe, DL.; España Cubillo, S.; Henánez-Lloreda, MV.; Hernández Orallo, J. (2011). Comparing humans and AI agents. En Artificial General Intelligence. Springer Verlag (Germany). 6830:122-132. https://doi.org/10.1007/978-3-642-22887-2_13S1221326830Dowe, D.L., Hajek, A.R.: A non-behavioural, computational extension to the Turing Test. In: Intl. Conf. on Computational Intelligence & multimedia applications (ICCIMA 1998), Gippsland, Australia, pp. 101–106 (1998)Gordon, D., Subramanian, D.: A cognitive model of learning to navigate. In: Proc. 19th Conf. of the Cognitive Science Society, 1997, vol. 25, p. 271. Lawrence Erlbaum, Mahwah (1997)Hernández-Orallo, J.: Beyond the Turing Test. J. Logic, Language & Information 9(4), 447–466 (2000)Hernández-Orallo, J.: A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. In: Hutter, M., et al. (eds.) 3rd Intl. Conf. on Artificial General Intelligence, pp. 182–183. Atlantis Press, London (2010) Extended report at, http://users.dsic.upv.es/proy/anynt/unbiased.pdfHernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18), 1508–1539 (2010)Hernández-Orallo, J., Dowe, D.L., España-Cubillo, S., Hernández-Lloreda, M.V., Insa-Cabrera, J.: On more realistic environment distributions for defining, evaluating and developing intelligence. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds.) AGI 2011. LNCS(LNAI), pp. 81–90. Springer, Heidelberg (2011)Legg, S., Hutter, M.: A universal measure of intelligence for artificial agents. In: Intl Joint Conf on Artificial Intelligence, IJCAI, vol. 19, p. 1509 (2005)Legg, S., Hutter, M.: Universal intelligence: A definition of machine intelligence. Minds and Machines 17(4), 391–444 (2007)Li, M., Vitányi, P.: An introduction to Kolmogorov complexity and its applications, 3rd edn. Springer-Verlag New York, Inc., Heidelberg (2008)Oppy, G., Dowe, D.L.: The Turing Test. In: Zalta, E.N. (ed.) Stanford Encyclopedia of Philosophy, Stanford University, Stanford (2011), http://plato.stanford.edu/entries/turing-test/Sanghi, P., Dowe, D.L.: A computer program capable of passing IQ tests. In: 4th Intl. Conf. on Cognitive Science (ICCS 2003), Sydney, pp. 570–575 (2003)Solomonoff, R.J.: A formal theory of inductive inference. Part I. Information and control 7(1), 1–22 (1964)Strehl, A.L., Li, L., Wiewiora, E., Langford, J., Littman, M.L.: PAC model-free reinforcement learning. In: ICML 2006, pp. 881–888. New York (2006)Sutton, R.S., Barto, A.G.: Reinforcement learning: An introduction. The MIT press, Cambridge (1998)Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)Veness, J., Ng, K.S., Hutter, M., Silver, D.: A Monte Carlo AIXI Approximation. Journal of Artificial Intelligence Research, JAIR 40, 95–142 (2011)von Ahn, L., Blum, M., Langford, J.: Telling humans and computers apart automatically. Communications of the ACM 47(2), 56–60 (2004)Watkins, C.J.C.H., Dayan, P.: Q-learning. Mach. learning 8(3), 279–292 (1992

Building Ethically Bounded AI

The more AI agents are deployed in scenarios with possibly unexpected situations, the more they need to be flexible, adaptive, and creative in achieving the goal we have given them. Thus, a certain level of freedom to choose the best path to the goal is inherent in making AI robust and flexible enough. At the same time, however, the pervasive deployment of AI in our life, whether AI is autonomous or collaborating with humans, raises several ethical challenges. AI agents should be aware and follow appropriate ethical principles and should thus exhibit properties such as fairness or other virtues. These ethical principles should define the boundaries of AI's freedom and creativity. However, it is still a challenge to understand how to specify and reason with ethical boundaries in AI agents and how to combine them appropriately with subjective preferences and goal specifications. Some initial attempts employ either a data-driven example-based approach for both, or a symbolic rule-based approach for both. We envision a modular approach where any AI technique can be used for any of these essential ingredients in decision making or decision support systems, paired with a contextual approach to define their combination and relative weight. In a world where neither humans nor AI systems work in isolation, but are tightly interconnected, e.g., the Internet of Things, we also envision a compositional approach to building ethically bounded AI, where the ethical properties of each component can be fruitfully exploited to derive those of the overall system. In this paper we define and motivate the notion of ethically-bounded AI, we describe two concrete examples, and we outline some outstanding challenges.Comment: Published at AAAI Blue Sky Track, winner of Blue Sky Awar

Considerations for comparing video-game AI agents with humans

Video games are sometimes used as environments to evaluate AI agents’ ability to develop and execute complex action sequences to maximize a defined reward. However, humans cannot match the fine precision of the timed actions of AI agents; in games such as StarCraft, build orders take the place of chess opening gambits. However, unlike strategy games, such as chess and Go, video games also rely heavily on sensorimotor precision. If the “finding” was merely that AI agents have superhuman reaction times and precision, none would be surprised. The goal is rather to look at adaptive reasoning and strategies produced by AI agents that may replicate human approaches or even result in strategies not previously produced by humans. Here, I will provide: (1) an overview of observations where AI agents are perhaps not being fairly evaluated relative to humans, (2) a potential approach for making this comparison more appropriate, and (3) highlight some important recent advances in video game play provided by AI agent

Accepting the Familiar: The Effect of Perceived Similarity with AI Agents on Intention to Use and the Mediating Effect of IT Identity

With the rise and integration of AI technologies within organizations, our understanding of the impact of this technology on individuals remains limited. Although the IS use literature provides important guidance for organization to increase employees’ willingness to work with new technology, the utilitarian view of prior IS use research limits its application considering the new evolving social interaction between humans and AI agents. We contribute to the IS use literature by implementing a social view to understand the impact of AI agents on an individual’s perception and behavior. By focusing on the main design dimensions of AI agents, we propose a framework that utilizes social psychology theories to explain the impact of those design dimensions on individuals. Specifically, we build on Similarity Attraction Theory to propose an AI similarity-continuance model that aims to explain how similarity with AI agents influence individuals’ IT identity and intention to continue working with it. Through an online brainstorming experiment, we found that similarity with AI agents indeed has a positive impact on IT identity and on the intention to continue working with the AI agent

Human- AI Collaboration: Cognitive Challenges in Interacting with Generative AI Agents

The recent advancements in the cognitive capabilities of generative artificial intelligence (Gen AI) agents have enabled them to transcend their role of merely a tool and instead act as a team member, capable of collaborating with human agents to accomplish complex tasks like creating images, writing codes and developing blogs. However, our understanding of human agents\u27 cognitive challenges while collaborating with Gen AI agents is limited. The present research investigates language-specific challenges that human agents face while interacting with Gen AI agents. In line with the linguistic perspective, our initial analysis indicates the emergence of three key language-specific challenges, namely syntactic challenge, semantic challenge, and pragmatic challenge. We expect the findings of this study will provide valuable insights into the emerging phenomenon of collaboration between human and Gen AI agents

VOICE UI FOR MULTIPLE AI AGENTS

We are standardizing UI for multiple voice agents on a PC device with a lightweight Voice App which will run as a background service on all consumer and commercial PC devices