CMP2020M Artificial Intelligence Dr. Bashir Al-Diri Dr. Marc Hanheide Dr. Patrick Dickinson Lincoln School of Computer Science CM2020M Artificial Intelligence Introduction to AI Knowledge Representation Planning Logic Programming(3/3) Games AI Probabilistic Reasoning Lecture 7: Logic Programming for AI • Review of Prolog Rules and Facts • Prolog • Matching […]
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CSC373H Lecture 4 Dan Zingaro October 3, 2016 Back to Activity Scheduling Input:setofactivitiesa1,a2,…,an Each activity ai has a start time si , finish time fi , and nonnegative profit (or worth) wi Output: subset of activities S such that no activities in S overlap and profit(S) is maximum Greedy Strategies This time,
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SCHEME and functional programming Dr. Jozo Dujmović ©Jozo Dujmović Functional Programming 1 Programming paradigms • Paradigm = fundamental style of programming. • Main computer programming paradigms are: 1. Procedural (imperative) programming 2. Nonprocedural (declarative) programming 3. Logic programming (subset of declarative programming) 4. Functional programming (subset of declarative programming) 5. Object-oriented programming ©Jozo Dujmović Functional
CMSC 330, fall 2016 Organization of Programming Languages Home Syllabus Schedule Projects Tests Resources Piazza Submit Server Grades Server Project 1 – WordNet Due Thursday, Sep 15, 2016 11:59pm Errata Updated on 08/31: length method description is modified for the case where it returns -1. Updated on 09/03: test public_outcast_outcast3.rb is updated. Introduction WordNet is
CMP2020M Artificial Intelligence Dr. Bashir Al-Diri Dr. Marc Hanheide Dr. Patrick Dickinson Lincoln School of Computer Science CM2020M Artificial Intelligence Introduction to AI Knowledge Representation Planning Logic Programming(1/3) Games AI Probabilistic Reasoning CMP2020M: Lecture 5 – Logic Programming My Lectures 1. Propositional Calculus 2. Predicate Calculus 3. Prolog Programming
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Games & AI #2 DIJKSTRA’S ALGORITHM Games & AI #2: Dijkstra’s Algorithm Patrick Dickinson This week 1. Look at an algorithm (Dijkstra’s) for solving general searches on graphs. 2. Try to understand how it works 3. Understand limitations 4. Think about how it could be used in games You will implement Dijkstra in workshop 2
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Lincoln School of Computer Science Assessment Component Briefing Document Title: CMP2020M Artificial Intelligence – Component One Indicative Weighting: 40% RESIT Learning Outcomes: On successful completion of this component a student will have demonstrated competence in the following areas: • [LO2] Apply Artificial Intelligence techniques to solve practical problems • [LO3] Locate and reference relevant information.
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CSC373H Lecture 2 Dan Zingaro September 19, 2016 Greedy Proof Technique Let S0 be our state before the first iteration of greedy Let S1 be our partial solution after the first iteration of greedy Let S2 be our partial solution after the second iteration of greedy . . . Let Sn
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CM3112 Artificial Intelligence Propositional logic Inference Steven Schockaert SchockaertS1@cardiff.ac.uk School of Computer Science & Informatics Cardiff University Entailment A formula β is entailed by a formula α if every model of α is also a model of β We write this as α ⊧ β If β is entailed by α we also say that
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CM3112 Artificial Intelligence Probability theory: Probabilities in AI Steven Schockaert SchockaertS1@cardiff.ac.uk School of Computer Science & Informatics Cardiff University Probabilities in AI Many planning problems involve actions with uncertain effects If I leave for the airport 150 minutes before the time of my flight, there is a 70% chance that I will get there in
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