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Semester 2

Informatics 2D - Reasoning and Agents (INFR08010)

Course Website

http://course.inf.ed.ac.uk/inf2d

Subject

Informatics

College

SCE

Credits

20

Normal Year Taken

2

Delivery Session Year

2023/2024

Pre-requisites

Course 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

Assessment Information

Written Exam 70%, Coursework 30%, Practical Exam 0%

Additional Assessment Information

The coursework component, worth 30% of the overall grade of the course, will consist of two assignments, worth 15% each.Exam 70%

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