Undergraduate Course: Informatics 2D - Reasoning and Agents (INFR08010)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Credits | 20 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://course.inf.ed.ac.uk/inf2d |
Taught in Gaelic? | No |
| Course description | 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. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2014/15 Semester 2, Available to all students (SV1)
|
Learn enabled: Yes |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
12/01/2015 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 30,
Seminar/Tutorial Hours 10,
Supervised Practical/Workshop/Studio Hours 17,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
137 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %
|
| 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
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. |
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 or Pearson, 2003. 2nd Edition.
* Thompson, S., "Haskell: The Craft of Functional Programming", Addison Wesley, 1999.
|
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Not entered |
Contacts
| 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 |
|
© Copyright 2014 The University of Edinburgh - 29 August 2014 4:10 am
|
University Homepage