Introduction to CLIPS C Language Integrated Production System Rules Wildcards

Introduction to clips c language integrated

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expert systems. Introduction to CLIPS (C Language Integrated Production System). Rules. Wildcards. Pattern matching. Pattern network. Join network. CLIPS expert system shell
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CS 561, Lecture 1 Course Overview (cont.) Logical Reasoning in the Presence of Uncertainty 22/23-Fuzzy logic. [Handout] Introduction to fuzzy logic. Linguistic Hedges. Fuzzy inference. Examples. Center of largest area Center of gravity
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CS 561, Lecture 1 Course Overview (cont.) AI with Neural networks 24/25-Neural Networks. [Handout] Introduction to perceptrons, Hopfield networks, self-organizing feature maps. How to size a network? What can neural networks achieve? x (t) 1 x (t) n x (t) 2 y(t+1) w 1 2 n w w axon
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CS 561, Lecture 1 Course Overview (cont.) Evolving Intelligent Systems 26-Genetic Algorithms. [Handout] Introduction to genetic algorithms and their use in optimization problems.
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CS 561, Lecture 1 Course Overview (cont.) What challenges remain? 27-Towards intelligent machines. [AIMA Ch 25] The challenge of robots: with what we have learned, what hard problems remain to be solved? Different types of robots. Tasks that robots are for. Parts of robots. Architectures. Configuration spaces. Navigation and motion planning. Towards highly-capable robots. 28-Overview and summary. [all of the above] What have we learned. Where do we go from here? [email protected]
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CS 561, Lecture 1 A driving example: Beobots Goal: build robots that can operate in unconstrained environments and that can solve a wide variety of tasks.
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CS 561, Lecture 1 Beowulf + robot = “Beobot”
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CS 561, Lecture 1 A driving example: Beobots Goal: build robots that can operate in unconstrained environments and that can solve a wide variety of tasks. We have: Lots of CPU power Prototype robotics platform Visual system to find interesting objects in the world Visual system to recognize/identify some of these objects Visual system to know the type of scenery the robot is in We need to: Build an internal representation of the world Understand what the user wants Act upon user requests / solve user problems
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CS 561, Lecture 1 Riesenhuber & Poggio, Nat Neurosci, 1999 The basic components of vision Original Downscaled Segmented + Attention Localized Object Recognition Scene Layout & Gist
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CS 561, Lecture 1
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CS 561, Lecture 1 Beowulf + Robot = “Beobot”
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CS 561, Lecture 1 Main challenge: extract the “minimal subscene” (i.e., small number of objects and actions) that is relevant to present behavior from the noisy attentional scanpaths. Achieve representation for it that is robust and stable against noise, world motion, and egomotion.
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CS 561, Lecture 1 Prototype Stripped-down version of proposed general system, for simplified goal: drive around USC olympic track, avoiding obstacles Operates at 30fps on quad-CPU Beobot; Layout & saliency very robust; Object recognition often confused by back ground clutter.
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CS 561, Lecture 1 Major issues How to represent knowledge about the world?
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