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- W4384694281 abstract "This dissertation proposes two novel approaches for extending AgentSpeak with qualitative uncertainty reasoning by integrating two dynamic variants of epistemic logic. These extensions address a crucial gap in the literature where qualitative approaches to uncertainty are seldom integrated into agent-oriented programming languages due to various challenges related to methodology, implementation, and computational complexity. The extensions provide various symbolic constructs that enable the modelling and reasoning of belief uncertainty. The significance of qualitative uncertainty reasoning is illustrated through a simple Minesweeper scenario and two complex uncertainty challenges from the 2019 Multi-Agent Programming Contest: uncertain navigation and agent identification. Given the ability to express qualitative uncertainty, we equip the agent with a more robust and effective way to plan and act under uncertainty. An in-depth evaluation of the performance and scalability of the proposed AgentSpeak extensions is provided, with a heavy focus on examining their impact on the agent’s time-sensitive reasoning cycle. The results show that these extensions provide a tractable and computationally feasible approach to extending AgentSpeak with the ability to manage the statics and dynamics of uncertainty." @default.
- W4384694281 created "2023-07-20" @default.
- W4384694281 creator A5078400481 @default.
- W4384694281 date "2023-07-18" @default.
- W4384694281 modified "2023-09-23" @default.
- W4384694281 title "Qualitative Uncertainty Reasoning in AgentSpeak" @default.
- W4384694281 doi "https://doi.org/10.22215/etd/2023-15560" @default.
- W4384694281 hasPublicationYear "2023" @default.
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