# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2015/2016

 University Homepage DRPS Homepage DRPS Search DRPS Contact
DRPS : Course Catalogue : School of Informatics : Informatics

# Undergraduate Course: Informatics 2D - Reasoning and Agents (INFR08010)

 School School of Informatics College College of Science and Engineering Credit level (Normal year taken) SCQF Level 8 (Year 2 Undergraduate) Availability Available to all students SCQF Credits 20 ECTS Credits 10 Summary This course focuses on approaches relating to representation, reasoning and planning for solving real world inference. The course illustrates the importance of (i) using a smart representation of knowledge such that it is conducive to efficient reasoning, and (ii) the need for exploiting task constraints for intelligent search and planning. The notion of representing action, space and time is formalized in the context of agents capable of sensing the environment and taking actions that affect the current state. There is also a strong emphasis on the ability to deal with uncertain data in real world scenarios and hence, the planning and reasoning methods are extended to include inference in probabilistic domains. Course description 1. Intelligent Agents: Introduction * Nature of agents, performance measures and environments * Wumpus World Problem : An example thread (Programming environment setup 2. Search based Planning * Planning as a Search Problem: In deterministic, observable, static and known environments * Smart Searching 1: Using constraints * Smart Searching 2: Exploiting subproblems/Memoisation * Informed Search and Exploration for agents 3. Logical Representation and Planning * Propositional Logic Revisited (Shortcomings) * First Order Logic & Encoding facts/rules in FOL * Inference Rules for Propositional & FOL Calculus * Unification and Generalized Modus Ponens * Resolution based Inference and directing search with it * Knowledge representation : Using FOL to represent action, space, time -- Wumpus Example * Situation Calculus: Representing time in plans 4. Scaling Planning for Complex Tasks * Representing States, Goals and Actions in STRIPS * Partial Order Planning * Planning and Acting in the Real World 5. Acting in Uncertain (real world) Environments * Representation with Bayes Net * Probabilistic Reasoning in Bayes Net * Planning under Uncertainity : Wumpus world revisited * Probabilistic Reasoning over Time I: hidden markov models * Probabilistic Reasoning over Time II: dynamic Bayesian networks * Markov Decision Processes Relevant QAA Computing Curriculum Sections: Artificial Intelligence, Human-Computer Interaction (HCI), Intelligent Information Systems Technologies, Simulation and Modelling
 Pre-requisites Students MUST have passed: Informatics 1 - Computation and Logic (INFR08012) AND Informatics 1 - Functional Programming (INFR08013) AND Informatics 1 - Data and Analysis (INFR08015) AND Informatics 1 - Object-Oriented Programming (INFR08014) It is RECOMMENDED that students have passed Informatics 2A - Processing Formal and Natural Languages (INFR08008) Co-requisites Prohibited Combinations Other requirements None
 Pre-requisites None High Demand Course? Yes
 Academic year 2015/16, Available to all students (SV1) Quota:  None Course Start Semester 2 Timetable Timetable Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 30, Seminar/Tutorial Hours 10, Supervised Practical/Workshop/Studio Hours 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 146 ) Assessment (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 % Additional Information (Assessment) In order to pass the course you must satisfy all of the following requirements: * achieve at least 35% in the examination; * achieve a total of at least 25% in assessed coursework; * obtain a combined total mark of at least 40% Assessment You should expect to spend approximately 50 hours on the coursework for this course. Feedback Not entered Exam Information Exam Diet Paper Name Hours & Minutes Main Exam Diet S2 (April/May) 2:00 Resit Exam Diet (August) 2:00
 1 - Use task constraints to make search efficient 2 - Perform Inference with First Order Logic 3 - Comprehend the strengths and weaknesses of various kinds of logic representations, e.g. Propositional, FOL 4 - Use STRIPS to plan and execute actions using either Propositional or First Order Logic representation. 5 - Create a Bayesian net representation of a non-deterministic planning problem 6 - Create a basic probabilistic action agent using simulated state transitions and goals
 * Russell, S. & Norvig, P., "AI: A Modern Approach", Prentice Hall or Pearson, 2003. 2nd Edition. * Thompson, S., "Haskell: The Craft of Functional Programming", Addison Wesley, 1999.
 Course URL http://course.inf.ed.ac.uk/inf2d Graduate Attributes and Skills Not entered Keywords Not entered
 Course organiser Dr Michael Rovatsos Tel: (0131 6)51 3263 Email: mrovatso@inf.ed.ac.uk Course secretary Ms Kendal Reid Tel: (0131 6)50 5194 Email: kr@inf.ed.ac.uk
 Navigation Help & Information Home Introduction Glossary Search DPTs and Courses Regulations Regulations Degree Programmes Introduction Browse DPTs Courses Introduction Humanities and Social Science Science and Engineering Medicine and Veterinary Medicine Other Information Combined Course Timetable Prospectuses Important Information
© Copyright 2015 The University of Edinburgh - 18 January 2016 4:12 am