Course Title : Artificial Intelligence
Code Course
Credits ECTS
CMP 314-1 A -1 2 0 2 3.00 5
Lecturer and Office Hours
Teaching Assistant and Office Hours
Course Level
Description The Artificial Intelligence course provides a general introduction to the basics of Artificial Intelligence. This course will cover the main techniques and methods of problem solving using AI by the use of logical reasoning agents, search methods, First-Order logic, logical reasoning systems, computational intelligence, artificial neural networks as well as genetic algorithms and programming.
Course Outline
1Introduction to AI This topic will address what AI is, the disciplines that contributed to AI, the history of AI, and the evolution of artificial intelligence today. Lit1, (Pages 1-33)
2Intelligent agents This topic will address agents and the environment, their perception, agent functions, agent programs, relational agent concept, omniscience, agent learning and autonomy, task environment definition, its features, agent structure, simple agents, model-based agents, goal-based agents, utility-based agents, learning agents and how the agent program component works. Lit1, (Pages 34-63)
3Problem solving through research This topic will address agents that solve well-defined problems, problem examples, real-world problems. Search for solution, tree method, search algorithm infrastructure, problem-solving performance measurement, uninformed search strategies, breadth-first search, uniform-cost search, depth-first search, depth-limited search, bidirectional search, comparison of uninformed search strategies, informed (heuristic) search strategies, best-first search, optimality condition (Acceptability and consistency), memory-bounded search, heuristic functions. Lit1, (Pages 64-119)
4Search Methods This topic will cover local search algorithm, hill-climbing search, local continuous space search, nondeterministic search, and-or search tree, partial preview search, online search agents, unknown environments, online search problems, online search agents and adversary search, games. Lit1, (Pages 120-201)
5Agents who reason logically In this topic will be treated agents who reason based on knowledge, logic, propositional logic, proof of propositional theorem, control of the effective proposition model and agents based on propositional logic. Lit1, (Pages 234-284)
6First-Order Logic (FOL) This topic will address the language of representation, the language of thought, the best combination of formal and natural languages, the syntax and semantics of FOL, the logic of the FOL model, the use of FOL logic, the engineering of knowledge in FOL. Lit1, (Pages 285-321)
7Logical reasoning systems This topic will address propositional inference versus First-order, reduction in propositional inference, First-order infertility rule, Forward-chaining and backward-chaining algorithms. Lit1, (Pages 322-365)
8Midterm Exam
9Planning This topic will address the definition of classical planning, planning algorithms such as state-space search, planning graphs, other approaches to classical planning, their analysis, planning and action in the real world, timeframes and resources, hierarchical planning, planning and action in undefined areas, multiagent planning. Lit1, (Pages 366-436)
10Computational Intelligence (CI) In this topic will be an introduction to the main problem classes for computational intelligence (CI) techniques, neural networks, fuzzy systems, evolutionary computing, Swarm intelligence. Lit2, (Pages 1-27)
11Artificial Neural Networks with Matlab / Python This topic will address the history of neural networks, artificial neural networks, electronic implementation of artificial neuron, neural network components, neural network architecture and algorithm, layered architecture and predictive networks. Lit2, (Pages 29-106)
12Evolutionary computation paradigms This topic will cover the history of evolutionary computation, the flowchart of a typical evolutionary algorithm, evolutionary computation models, genetic algorithm, genetic programming, evolutionary programming, evolutionary strategy, advantages and disadvantages of evolutionary computation. Lit2, (Pages 419-544)
13Matlab / Python based genetic algorithm This topic will address the history, description and the role of genetic algorithm, its parameters, construction of block hypotheses, dynamism of a scheme, illustrations based on scheme theorem, cross operations, 1-point intersection, 2-point intersection and other operations in genetic algorithm. Lit 2, (Pages 547-588)
14Genetic programming This topic will address the LISP programming language, genetic programming functionality, genetic programming functionalities, creating a random population, functions and terminals, genetic operations, selection functions, cross operations, genetic programming in machine language, basics of genetic programming, genetic programming flowchart and advantages of genetic programming. Lit 2, (Pages 591 -646)
15General Review
16Final Exam
Other References
Laboratory Work
Computer Usage
Learning Outcomes and Competences
1Studentët do të jenë të aftë të kuptojnë çfarë është Inteligjenca Artificiale si dhe evolimin e saj.
2Studentët do të kenë njohuri mbi konceptet kryesore të Inteligjencës Artificiale.
3Studentët do të përvetësojnë metodat dhe teknikat kryesore të zgjidhjes së problemeve me anë të Inteligjencës Artificiale.
4Studentët do të jenë të aftë të implementojnë metodat dhe teknikat kryesore të zgjidhjes së problemeve me anë të Inteligjencës Artificiale në Matlab/Python.
5Studentët do të jenë të gatshëm për t’u bërë pjesë e diskutimeve të frytshme në fushën e evolimit të metodave dhe teknikave kryesore të përdorura nga Inteligjenca Artificiale.
6Studentët do të jenë të pajisur me metodat dhe teknikat e mjaftueshme të përdorura nga Inteligjenca Artificiale për të vijuar me lëndët e tjera pasardhëse.
Course Evaluation Methods
In-term studies Quantity Percentage
Term Projects00
Contribution of in-term studies to overall grade60
Contribution of final examination to overall grade40
ECTS (Allocated Based on Student) Workload
Activities Quantity Duration
Total Workload
Course Duration (Including the exam week : 16 x Total course hours) 16464
Hours for off-the-classroom study (Pre-study, practice) 14456
Assignments 144
Midterms 155
Final examination 155
Other 000
Total Work Load 134
Total Work Load / 25 (hours) 5.36

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