| CODE | ARI2101 | ||||||||||||
| TITLE | Fundamentals of Automated Planning | ||||||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||||||||
| MQF LEVEL | 5 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||
| DESCRIPTION | This unit will introduce the field of Automated Planning (also known as AI Planning). This field deals with finding the actions that need to be applied, and the ordering constraints between them, to achieve some goal. It is applicable in various areas where intelligent automation or autonomy is required. Various industries make use of automated planning, such as Space, Energy, Transport & Logistics, and Oil & Gas. This unit will present the architecture of an autonomous system and the role of automated planning in reasoning about the possible actions that can be performed. The definition of a planning problem will be introduced together with the formalisms to represent states and actions. An overview of the different flavours of planning formalisms such as STRIPS, Classical Planning, Numeric Planning and Temporal Planning will also be provided. The unit will cover the main data structures and algorithms used for automated planning, including Graphplan, forward state-space search such as A* and Hill Climbing, and heuristics such as delete-relaxation and landmarks. The algorithmic complexity of planning will also be analysed. The Planning Domain Definition Language (PDDL) will also be covered by this course, with practical experience of using PDDL with some off-the-shelf automated planners. Study-unit Aims: This study-unit aims to introduce students to the AI field of Automated Planning. It will provide students with enough knowledge to be able to recognize the characteristics, and successfully model a planning problem, both formally and also using PDDL. It will also present the architecture of an autonomous system and the role of automated planning in reasoning about the actions that should be performed to achieve some goal. It aims to provide students with sufficient understanding of the main data structures and algorithms used in typical AI Planning systems, with a thorough understanding of the advantages and disadvantages of each technique, together with the algorithmic complexity involved. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - Create a simple automated planning system using some of the search algorithms described during the course; - Model a Planning problem formally and using PDDL; - Use an existent off-the-shelf automated planning system to solve problems described in PDDL; - Analyse the algorithmic complexity of planning problems. 2. Skills By the end of the study-unit the student will be able to: - Implement various data structures and algorithms used in Automated Planning; - Model problems using PDDL; - Analyse and characterise different planning problems; - Use off-the-shelf automated planners for specific types of planning problems. Main Text/s and any supplementary readings: - Ghallab M., Nau D., and Traverso P., "Automated Planning and Acting", Cambridge University Press, 2016, ISBN-13: 978-1107037274 Supplementary readings: - Russell S. and Norvig P., "Chapter 10 - Classical Planning and Chapter 11 - Planning and Acting in the Real World" in "Artificial Intelligence: A Modern Approach", Pearson, 3rd Edition, 2016. ISBN-13: 978-1292153964 - Poole D.L., and Mackworth A. K. "Chapter 6 - Planning with Certainty and Chapter 10 - Planning with Uncertainty" in "Artificial Intelligence: Foundations of Computational Agents", Cambridge University Press, 2nd Edition, 2017, ISBN: 9781107195394, online: https://artint.info/ - Bonet B., and Geffner H., "A Concise Introduction to Models and Methods for Automated Planning", Morgan & Claypool Publishers, 2013, ISBN-13: 978-1608459698 - Ghallab M., Nau D., and Traverso P., "Automated Planning: Theory & Practice", Morgan Kaufmann, 2004. ISBN-13: 978-1493303700 |
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| ADDITIONAL NOTES | Pre-Requisite Study-Units: ICT1018 and ICS1019 | ||||||||||||
| STUDY-UNIT TYPE | Lecture, Independent Study and Project | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Josef Bajada |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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