Thesis Objectives and Contributions Sample Clauses

Thesis Objectives and Contributions. ‌ We set ourselves three main objectives that relate to planning and memory-based systems: The first objective – O1 – is to investigate the field of cost-optimal classical planning, looking at how best to represent a planning state and methods of combining several heuristics into one best suited for the task given. We will do this by using Pattern Databases (PDBs), an abstraction-based heuristic that has shown great performance across different domains. This combined with a symbolic representation of the planning state, combine into state-of- the-art performance across all modern planning benchmarks. Early versions of the techniques revolving around PDBs were submitted as independent planners to the Cost-Optimal track from the 2018 deterministic International Planning Competition (IPC), in which it proved as the most powerful novel search approach available at that time. The second objective – O2 – is to do an analysis of the Cost-Optimal track from the 2018 Deterministic International Planning Competition (IPC), where as stated previously, our two planners finished inside the top four, both within 2% distance from the winner. The IPC is an important event for the planning community, as it is a fair evaluation of the field as a whole, and a good analysis of it will lead to advances and important information regarding what are the new best approaches to solving domain independent planning. We identify this competition as a valuable source of information and practical solutions to planning as a whole. This, together with the basis of PDBs and Pattern Selection, directed us to shift our focus into AI planning for robotics and research how to best use deterministic planning in dynamic environments. Lastly, the third objective – O3 – directly follows the work presented in the first two parts, and is aiming at developing a method that could leverage past solutions in executions for aiding agents that need to reason in environments with uncertainty. The main contributions of this thesis are as follows: • C1 – Improve cost-optimal planning with Pattern Databases, as this method takes most advantage of the memory gains from the hardware of the planning system; • C2 – Evaluate different Pattern Selection systems, with the aim of finding the best combination of solutions, while simplifying the creation process; • C3 – Define portofolio-planning, which was the most interesting revelation from the cost-optimal track of the deterministic IPC. The good performance from t...
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Related to Thesis Objectives and Contributions

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