The Essence of Intelligence is Appropriate Action (not thinking, reasoning, learning or language) and other things every student of AI should know

An agent acts in its world to achieve its objectives. Intelligence allows the agent to make decisions and act. In natural domains, sensing is limited, so acting is gambling. It’s a myth that passive learning and more data are all we need. An agent cannot learn from observations alone. It needs a real body to carry out experiments in its world, testing hypotheses, to determine causation, refining its model of the world’s dynamics. The agent is acting as a scientist: refining its model through experiments and acting appropriately to achieve its objectives. Its objectives depend on its preferences and values, and those of other agents its actions impact. Determining which values to use, and how preferences can be acquired fairly, is a major non-technical challenge. We address three primary questions: What should an agent believe? What should an agent do, given its beliefs, preferences, and abilities? What should the preferences of an agent be? Integrating these issues motivates the design of our latest AI textbook, Artificial Intelligence: Foundations of Computational Agents, (3rd Ed. 2023). Alan Mackworth is a Professor Emeritus of Computer Science at the University of British Columbia. He works on artificial intelligence with applications in constraint satisfaction, cognitive robotics, assistive technology, hybrid systems and constraint-based agents. He invented the world’s first soccer-playing robots. He has co-authored two books: Computational Intelligence: A Logical Approach (1998) and Artificial Intelligence: Foundations of Computational Agents (2023, 3rd Ed.). Alan co-founded the UBC Cognitive Systems Program, the Centre for AI, Decision-making and Action (CAIDA) and the AI network of BC (AInBC). He has served as President of AAAI, IJCAI and CAIAC. He is a Fellow of AAAI, CAIAC, AGE-WELL, CIFAR and the Royal Society of Canada. David Poole is a Professor Emeritus of Computer Science at the University of British Columbia. He is known for his work on combining logic and probability, probabilistic inference, relational probabilistic models, statistical relational AI and semantic science. He is a co-author of two AI textbooks (Cambridge University Press, 3rd edition 2023, and Oxford University Press, 1998), and co-author of ” Statistical Relational Artificial Intelligence: Logic, Probability, and Computation”. He is a former chair of the Association for Uncertainty in Artificial Intelligence, the winner of the Canadian AI Association (CAIAC) 2013 Lifetime Achievement Award, and is a Fellow of the Association for the Advancement Artificial Intelligence (AAAI) and CAIAC.