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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2012/2013
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DRPS : Course Catalogue : School of Informatics : Informatics

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

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 8 (Year 2 Undergraduate) Credits20
Home subject areaInformatics Other subject areaNone
Course website https://www.inf.ed.ac.uk/teaching/courses/inf2d Taught in Gaelic?No
Course descriptionThis 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.
Entry Requirements (not applicable to Visiting Students)
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
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2012/13 Semester 2, Available to all students (SV1) Learn enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 15:00 - 15:50
CentralLecture1-11 15:00 - 15:50
CentralLecture1-11 15:00 - 15:50
First Class Week 1, Tuesday, 15:10 - 16:00, Zone: Central. Lecture Theatre 270, Old College
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)2:00
Resit Exam Diet (August)2:00
Summary of Intended Learning Outcomes
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
Assessment Information
Written Examination 75
Assessed Assignments 25
Oral Presentations 0

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
Three pieces of assessed coursework. Details will change every year and will be published on the course web pages.

Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus 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
Transferable skills Not entered
Reading list * Russell, S. & Norvig, P., "AI: A Modern Approach", Prentice Hall, 2003. 2nd Edition.
* Thompson, S., "Haskell: The Craft of Functional Programming", Addison Wesley, 1999.
* Sigmon, K. & Davis, T. A., "MATLAB Primer", Chapman & Hall/CRC, 2001. 6th Edition.
An Introduction to Multiagent Sysrems, 2nd Edition, Michael Woolridge, Wiley
Study Abroad Not entered
Study Pattern Lectures 30
Tutorials 9
Timetabled Laboratories 18
Non-timetabled assessed assignments 50
Private Study/Other 93
Total 200
KeywordsNot entered
Contacts
Course organiserProf Colin Stirling
Tel: (0131 6)50 5186
Email: cps@inf.ed.ac.uk
Course secretaryMs Kendal Reid
Tel: (0131 6)50 5194
Email: kr@inf.ed.ac.uk
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