behavior-based ai amir massoud farahmand

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  • Behavior-based AI Amir massoud Farahmand
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  • Happy birthday to Artificial Intelligence 1941 Konrad Zuse, Germany, general purpose computer 1943 Britain (Turing and others) Collossus, for decoding 1945 ENIAC, US. John von Neumann a consultant 1946 The Logic Theorist on JOHNNIAC--Newell, Shaw and Simon 1956 Dartmouth Conference organised by John McCarthy (inventor of LISP) The term Artificial Intelligence coined at Dartmouth--- intended as a two month, ten man study!
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  • HP to AI (2) It is not my aim to surprise or shock you----but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to these things is going to increase rapidly until........ (Herb Simon 1957) Unfortunately, Simon was too optimistic!
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  • What AI has done for us? Rather good OCR (Optical Character Recognition) and Speech recognition softwares Robots make cars in all advanced countries Reasonable machine translation is available for a large range of foreign web pages Systems land 200 ton jumbo jets unaided every few minutes Search systems like Google are not perfect but very effective information retrieval Computer games and autogenerated cartoons are advancing at an astonishing rate and have huge markets Deep blue beat Kasparov in 1997. The world Go champion is a computer. Medical expert systems can outperform doctors in many areas of diagnosis (but we arent allowed to find out easily!)
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  • AI: What is it? What is AI? Different definitions The use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular (Boden) The study of intelligence independent of its embodiment in humans, animals or machines (McCarthy) AI is the study of how to do things which at the moment people do better (Rich & Knight) AI is the science of making machines do things that would require intelligence if done by men. (Minsky) (fast arithmetic?) Is it definable?! Turing test, Weak and Strong AI and
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  • AI: Basic assumption Symbol System Hypothesis: it is possible to construct a universal symbol system that thinks Strong Symbol System Hypothesis: the only way a system can think is through symbolic processing Happy birthday Symbolic (Traditional Good old-fashioned) AI
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  • Symbolic AI: Methods Knowledge representation (Abstraction) Search Logic and deduction Planning Learning
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  • Symbolic AI: was it efficient? Chess [OK!] Block-worlds [OK!] Daily Life Problems Robots [~OK!] Commonsense [~OK!] [~OK]
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  • Symbolic AI and Robotics Functional decomposition Sequential flow Correct perceptions is assumed to be done by vision-researched in a a-good-and-happy-will-come-day! Get a logic-based or formal description of percepts Apply search operators or logical inference or planning operators Perception Task execution Planning World Modelling Motor control sensors actuators
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  • Challenges of real robotic system Sensors and Effectors Uncertainty Partial Observability of Environment Non-stationarity
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  • Requirements of control system of an intelligent autonomous mobile robot Multiple-goal Robust Multiple-sensors Extensible [Learning]
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  • Behavior-based approach to AI Behavioral (activity) decomposition [against functional decomposition] Behavior: Sensor->Action (Direct link between perception and action) Situatedness Embodiment Intelligence as Emergence of
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  • Behavioral decomposition build maps explore avoid obstacles locomote manipulate the world sensors
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  • Situatedness No world modelling and abstraction No planning No sequence of operations on symbols Direct link between sensors and actions The world is its own best model
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  • Embodiment Only an embodied agent is validated as one that can deal with real world. Only through a physical grounding can any internal symbolic system be given meaning
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  • Emergence as a route to Intelligence Emergence: interaction of some simple systems which results in something more than sum of those systems Intelligence as emergent outcome of dynamical interaction of behaviors with the world
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  • Behavior-based design Robust not sensitive to failure of particular part of the system no need for precise perception as there is no modelling there Reactive: Fast response as there is no long route from perception to action No representation
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  • A Simple problem Goal: make a mobile robot controller that collects balls from the field and move them to home What we have: Differentially controlled mobile robot 8 sonar sensors Vision system that detects balls and home
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  • Basic design move toward ball move toward home exploration avoid obstacles
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  • A simple shot
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  • Different Controlling Mechanism Reactive Fast, Suitable for structured & a priori known environment No internal representations Deliberative Not practical (uncertainty, huge search spaces and ) Hybrid Behavior-based
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  • How can we automate it?! How?!
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  • Matarics Main Trends in Behavior-based Learning Learning Behavior Policy Learning Models of the Environment Learning Models from Behavior History Michaud Learning Models of Interaction Goldberg Learning from Human and Other Agents Nicolescu
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  • Learning Behavior Policy They learn (condition,behavior) using RL framework. No Hierarchy No MDP Scaling up RL: Reward Shaping (progress estimator) Reward Sharing Social Learning Perceptual Sharing
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  • Learning Behavior Policy (2) Mataric, RL in the multi-robot domain, 1997. Mataric, Reward function for accelerated learning, 1994. Mataric, Learning social behaviors, 1997. Mataric, Using communication to reduce locality in distributed multi-agent learning, 1997 Simsarian and Mataric, Learning to cooperate using two six-legged mobile robots, 1995. Others Maes and Brooks, Learning to coordinate behaviors, 1990. Mahadevan and Connel, Scaling RL to robotics by exploiting the SSA, 1991.
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  • Learning Models of the Environment They used BBS to learn env. map to show that it is possible for BB structure as a representator mechanism. Map Building Localization Path planning
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  • Learning Models from Behavior History Store active behaviors in tree-like structure in which each link shows transition probability. Reduce interference by recognizing common patterns of interference. Michaud and Mataric, Learning from history for behavior- based mobile robots in non-stationary conditions, 1998. Michaud and Mataric, Representation of behavioral history for learning in non-stationary conditions
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  • Learning Models of Interaction Augmented Markov Model It is a kind of HMM without any hidden state. Used in order to model behavior transition probability
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  • Learning Models of Interaction (2) Why should we use AMM? No a priori knowledge from env. Local sensing (need time to estimate env.) Non-stationary variability How can it help? Deriving useful statistics from AMM such as mean first pass between two behavior
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  • Learning Models of Interaction (3) In a demining task with different values of mine, extract demining time with AMM in order to maximize reward. Multi-agent Individual performance evalutation (fault detection) Group affiliation Group performance
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  • Learning Models of Interaction (4) Dani Goldberg and Maja Mataric, Augmented Markov Models (Tech. Report) Goldberg and Mataric, Coordinating mobile robot group behavior using a model of interaction dynamics, 1999. Goldberg and Mataric, Learning multiple models for reward maximization, 2000.
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  • Abstract Behaviors Main deficiencies of basic BBS It has no symbolic and abstract representation There is no ease of reusability and changing during operation
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  • Abstract Behaviors (2)
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  • Learning from Human and Other Agents Learning from demonstration Use AB structure
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  • Learning from Human and Other Agents (2) Learning is consisted of Store activated behaviors during demonstration Set preconditions of NAB
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  • Learning from Human and Other Agents (3) Monica Nicolescu and Maja Mataric, Extending behavior-based systems capabilities using an abstract behavior representation, 2000. Nicolescu and Mataric, A hierarchical architecture for behavior-based robots, 2002. Nicolescu and Mataric, Natural methods for robot task learning: Instructive demonstration, generalization, and practice, 2003
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  • Learning Behavior learning How should a single behavior act? Structure learning How should behaviors arranged in architecture?
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  • Overview of learning methods common to Compute Science Supervised learning R


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