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Advanced AI Course:
 Graph Search Algorithms and Applications 
Prof. Oren Salzman  
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Graph search algorithms are at the core of many real-world problems, from robot motion planning to transformer decoding and large-scale path planning in road networks. While fundamental algorithms like Dijkstra’s and A* are well known and widely used, real-world applications often introduce unique computational challenges. This course explores how to adapt and extend these algorithms to address complex constraints and optimization goals in diverse domains.

This course is designed for professionals and researchers in AI, robotics, machine learning, and operations research who want to enhance their understanding of graph search algorithms and their applications. A background in basic search algorithms (such as A*) is beneficial. 

July 7-8, 2025
9:00-16:00
Technion, Haifa

Course Overview


This course provides a deep dive into modern graph search techniques, bridging the gap between classical AI search methods and advanced algorithmic strategies used in robotics, machine learning, and network optimization. We will revisit fundamental algorithms and progressively move towards state-of-the-art approaches designed to handle computationally expensive cost functions, inadmissible heuristics, multi-objective optimization, and real-time constraints.


Preliminary Syllabus


  • Module 1: Introduction to Search Algorithms

Problem formulation in graph search
Blind search vs. informed search
Bounded-suboptimal search methods

  • Module 2: Heuristics in Graph Search

The role of heuristics in AI search
Multi-Heuristic A* and advanced heuristic techniques

  • Module 3: Lazy Search Strategies

Computational challenges in real-world applications
LazySP, LazywA*, and LRA*

  • Module 4: Multi-Objective Search

Pareto frontier and dimensionality reduction
Bi-objective search and approximation algorithms

  • Module 5: Application in Robotics – Contact-Aided Navigation

Algorithmic adaptations for robot motion planning
Case studies in real-world robotic systems

  • Module 6: Application in Robotics – Inspection Planning

Search techniques for robotic exploration
Optimizing efficiency and coverage in inspection tasks

  • Module 7: Application in Transformer Decoding

How search algorithms enhance transformer-based language models
Practical considerations in sequence decoding

  • Module 8: Application in Vehicle Routing

Multi-objective path planning in transportation networks
Real-world implementation and case studies



Why Take This Course?


  • Gain hands-on knowledge of advanced graph search techniques
  • Learn from real-world applications in robotics, AI, and optimization
  • Understand how to tailor search algorithms to complex, computationally demanding scenarios
  • Enhance your problem-solving skills in AI search methodologies


Whether you are a researcher, engineer, or AI enthusiast, this course will provide you with tools and insights to leverage advanced graph search algorithms for your applications.


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Prof. Oren Salzman is a faculty member in the Computer Science Department at the Technion, where he leads the Computational Robotics Lab (CRL). His research focuses on algorithmic challenges in robotic motion planning and search-based planning. He earned his Ph.D. from Tel Aviv University and completed a postdoctoral fellowship at Carnegie Mellon University. Prof. Salzman’s work advances graph search algorithms and their applications in robotics and AI. 
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Tuition fee:
  • Regular: 3,900 ILS
  • Industry Affiliation Partners: 2,700 ILS 
Industrial Affiliates Program (IAP) 
** prices are not ncluding VAT **

For any question or additional information, please contact: [email protected] 
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