Facultatea de Automatica si Calculatoare

University POLITEHNICA of Bucharest

Department of Computer Science


Knowledge Representation and Reasoning

Fall 2010



Course information                                                                                                    




Course description


































Course grades
Mid-term exam               20%
   Final exam                     30%

   Projects                         30%
   Laboratory                     20%


      - Min 7 lab attendances, min 50% of term activity (mid-term ex, projects, lab)

      - Reading materials. You are expected to do the readings before the class.



S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach, Prentice Hall, 2009, http://aima.cs.berkeley.edu/

D. Poole, A. Mackworth, R. Goebel. Computational Intelligence – a Logical Approach. Oxford University Press, 1998.



R. Brachman, H. Levesque. Knowledge Representation and Reasoning. The Morgan Kaufmann Series in Artificial Intelligence, 2004



KRR projects


To be announced


AI-MAS recommended links


See here relevant KRR resources


Academic Honesty Policy

It will be considered an honor code violation to give or use someone else's code or written answers, either for the assignments or exam tests.

If such a case occurs, we will take action accordingly.

Professor: Adina Magda Florea
Teaching assistant: Cristian Gratie

Class: Monday, 16-18, Room EC101

Office hours: Monday 18-19, Tuesday 17-18, Room EF201

A course in

UPB Master of Science Programme in Artificial Intelligence


This course teaches you models and advanced techniques of knowledge representation and reasoning in AI systems, including models for knowledge representation for the Semantic Web. The course focuses on modern methods and technologies, which allow building „intelligent” programs by representing relevant knowledge of the problem domain and taking optimal decisions towards successful resolution of these problems. Existing representational frameworks developed within AI, their key concepts and inference methods, and relevant applications are presented, to form a coherent picture of what KR is.



1. General knowledge representation issues


Logic and Artificial Intelligence at SEP

2. Logical agents – Logical knowledge representation and reasoning


         First order predicate logic, ATP (Slides)

AIMA Chapter 7, FOPL, Basics (RO)

         Nonmonotonic logics and reasoning (Slides-1, Slides-2)

Non-monotonic Logic at SEP, Nonmonotonic Reasoning, Nonmonotonic Reasoning With WebBased Social Networks

         Modal logic, logics of knowledge and belief (Slides)

Modal logic on Wikipedia, Possible Worlds, Belief, and Modal Logic: a Tutorial

         Semantic networks and description logics, reasoning services (Slides)

An Introduction to Description Logics

         Knowledge representation for the Semantic Web (Slides)

Description Logics as Ontology Languages for the Semantic Web

                 Midterm exam


3. Rule based agents


         Rete: Efficient unification

The RETE algorithm

         The Soar model, universal subgoaling and chunking (Slides-1, Slides-2)

A Gentle Introduction to Soar, an Architecture for Human Cognition

         Modern rule based systems

Jess,  Writing Rules for the Semantic Web Using SWRL and Jess

4. Probabilistic agents


        Probabilistic knowledge representation and reasoning (Slides)

Belief Network Inference Algorithms,  An Overview of Bayesian Network-based Retrieval Models

        Rule based methods for uncertain reasoning

Probabilistical Reasoning Systems

5. Intelligence without representation vs. Strong AI

Class debate

               Final exam