Artificial Intelligence

Presentation Knowledge representation and reasoning are essential components of an intelligent system and are at the core of Artificial Intelligence research. The aim of this course is to provide the students with several computational concepts and tools that have been developed in logic programming to support symbolic reasoning. The material covered in the course will interleave the computational concepts of logic programming with applications of the concepts in knowledge representation and problem solving. The students will have the opportunity to learn the general framework of Answer Set Programming (ASP) that interprets logic programs under the Answer Set Semantics to solve tasks such as (1) Defeasible reasoning, (2) Solving combinatorial problems, (3) Solving optimization problems using preferences, and (4) Reasoning about actions and change, The course will conclude by introducing basic notions of abduction and induction in ASP as alternative framework to statistical machine learning to do symbolic learning. Students will have the opportunity to put into practice the topics learned in class by solving simple problems using an ASP environment and an Inductive Learning system that learns ASP programs. Associated skills • That students have and understand knowledge that provides a basis or opportunity to be original in the development and/or application of ideas, often in a research context. • That students are able to integrate knowledge and face the complexity of making judgments from information that, being incomplete or limited, includes reflections on the social and ethical responsibilities linked to the application of its knowledge and judgments. • That students possess the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous. • Applying with flexibility and creativity the acquired knowledge and adapting it to new contexts and situations. Learning outcomes Write ASP programs that use constraints and choice operators Represent and solve NP-hard combinatorial problems in ASP Solve optimisation problems using preferences Model complex planning environments using logic-based action description languages Translate these planning environments into ASP programming to solve different types of planning tasks Write specifications of Inductive Learning tasks and implement them in an Inductive Learning system