Courses Bachelor Display 20242025
Course Description  To PDF  

Course title  Reasoning Techniques  
Course code  BENC2023  
ECTS credits  5,0  
Assessment  Whole/Half Grades  
Period 


Level  no level  
Coordinator 
Mark Winands For more information: m.winands@maastrichtuniversity.nl 

Language of instruction  English  
Goals 
* Knowledge and understanding: Students learn to understand how problems can be represented as logical problems, as search problems, as planning problems or as problems involving uncertainty and get accustomed to reasoning methods to solve problems of all four types mentioned above.
* Applying knowledge and understanding: Students learn to apply the reasoning methods learned to toy problems and some more complex situations. * Making judgements: Students learn to judge which type of knowledge representation is suitable for the problem at hand, and which reasoning technique is suitable to solve the problem at hand. * Communication: students can explain the knowledge representation used and reasoning technique chosen to peers and other experts. * Learning skills: Students are able to critically reflect on their own and otherâ€™s chosen representations and used reasoning methods. 

Description 
Central in this course is how, based on available data, new knowledge and information can be obtained using reasoning processes. The course will be supported by tutorials, in which the acquired techniques can be put into practice by using Prolog. The following four techniques are discussed:
(1) Reasoning using logic: syntax, semantics, and inference in firstorder logic, situation calculus, forward and backward reasoning, completeness, logic programming with Prolog. (2) Problem solving using search: problem types, blindsearch methods, informedsearch methods, comparison of search methods, games as search problems, minimax, alphabeta pruning, Monte Carlo Tree Search, chance games, constraint satisfaction problems. (3) Planning: planning in situation calculus, representation of states, goals and operators, state space and plan space, algorithms for classic planning. (4) Reasoning with uncertainty: uncertainty and probability theory, conditional probability, the Rule of Bayes, semantics of belief networks, exact and approximate inference in belief networks. 

Literature 
Study material:
* Russell, S. and Norvig, P., Artificial Intelligence: A Modern Approach, 4th edition. Pearson, 2020. * Bratko, I. (2012). Prolog: Programming for Artificial Intelligence, 4th edition. AddisonWesley Recommended literature: * Luger, G.F., Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edition. Pearson International Edition, 2009. 

Prerequisites 
BENC1002 Calculus
BENC2001 Multivariable Calculus BENC1004 Linear Algebra 

Keywords 


Teaching methods (indicative; course manual is definitive)  Lecture  
Assessment methods (indicative; course manual is definitive)  Written Exam / Assignment  
Evaluation in previous academic year  For the complete evaluation of this course please click "here"  
This course belongs to the following programmes / specialisations 
