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Chapter 2 Alper Aydemir CVAP, KTH Origins of Artificial Intelligence • Classical approach: “the brain” is the one and only holy grail of AI • 1956 Darthmouth meeting – Good old Fashioned AI • Expert systems were thought of replacing human intelligence “soon” • Symbol processing, set of rules, when we have enough rules success! Origins of Artificial Intelligence • The world state can be described as a configuration of clear, unambiguous set of symbols. – This is how computers work, everything should be as precise as possible: not in nature! • The more complex the model is the more care to maintain it • GOFAI has failed to live up to its promises • Let alone human-like, even insect-like intelligence is not achieved yet Origins of Artificial Intelligence • Common sense: “surely a hallmark of intelligence” (same as chess?) • Very large set of “rules” is crucial to teach robots common sense • Speech is also another area where GOFAI failed – Google Voice • Asking the right question Robots with bodies • Brooks: “The world itself is its own best model” – Studying insects • Simply act and react, no need to model the world to its atoms – Decision theoretic planning super expensive • Truly autonomous robots need to learn the environment on their own – Kuiper’s garbled pixels Robots with bodies • Interaction with the real world is never clean but messy and ill defined. – Modeling everything is simply impossible • Effect of embodiment idea on AI – Research on animal intelligence – More collaboration with biology, neuroscience Neuroscience • 80’s: The rise of Artificial Neural Networks – Simulation/abstraction neurons and their connections – Some success in computer vision (classification, pattern recognition) and language acquisition • Connectionist approach – “I am my connectome”, S. Seung • However this was mostly without embodiment • Recently, taken interest in embodiment, neuroscience gained popularity again – Computational Neuroscience, Neuro-Informatics etc. Multidisciplinary AI • Computer Science • Linguistics / Computational linguistics • Philosophy – A. Sloman • Biology, robotics, biomechanics • Embodied AI called robotics, biomimetics, adaptive locomotion et cetera. • Various conferences sprung up Biorobotics • Build robots which mimics certain organisms (typically simple and non-human) • Example: Sahabot mimicking Tunisian desert ants • Snapshot model by Sussex Univ. – Short-range navigation based on horizon appearance – Long-range navigation based on light polarization – Empirically proven model Biorobotics • No need for a detailed map, course, completely imprecise navigation and localization – SLAM! • Snakebots by Hirose Lab. – Univ. of Tokyo • Auke Ijspeert’s salamander – EPFL • ... Developmental Robotics • Brooks falsely claimed “we have reached insectlike intelligence” and moved to a “sexier” topic: how does human learning work? • Humanoid robotics became popular in Japan – However mostly concentrated in mechanics • With Brook’s “Cog” project a new interest in human-like intelligence flamed up • HRP humanoid project • Ishiguro’s baby robot, RoboCub Ubiquitous computing • Moving away from interacting with computers with only mouse/keyboard • “Scatter” sensors everywhere • More recently researchers became interested in not only sensing but changing the world as well • Various ways of interacting with computers, “wearable computers” and so gained popularity Multi-agent systems • Important insight: complex behavior can emerge from very simple rules in the individual level • Cellular automata: the Game of life, movement of bird flocks • Swarm intelligence, self-organization – Book: “Order out of Chaos” by Prigogine Self organization • Emergence of a pattern from the local interactions of many individuals • Ant trails – Marked by pheromones – P(followingTrail(X)) ~ trail scent – Shorter paths = more ants prefers shorthest food source gets consumed first! • Modular robotics, self-configuration and selfassembly • Murata, Tokyo Inst. of Technology Multi agent systems • Rather than emergence of pattern aimed at solving a particular task • RoboCup: a team of robots playing soccer – Various leagues: small, humanoid, middle – 100.000 spectators in Fukuoka match! Evolutionary Robotics • Trying to understand and simulate natural evolution – Genetic algorithms for designing electronic circuits – Only “the brain” evolves so far in previous work, though there are some few examples of the body evolving. Summary • The journey of AI has changed significantly from GOFAI to embodied systems • Embodied intelligence is now the artificial intelligence (or has become) • By building synthetic systems (robots) we can learn a lot about the nature of intelligence • Also they allow testing of concrete ideas rather than just thought experiments – “Put your money where your mouth is!”