Abstraction of Agents Executing Online and their Abilities in the Situation Calculus
Authors: Bita Banihashemi, Giuseppe De Giacomo, Yves Lespérance
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a general framework for abstracting online behavior of an agent that may acquire new knowledge during execution (e.g., by sensing), in the situation calculus and Con Golog. We assume that we have both a high-level action theory and a low-level one that represent the agent s behavior at different levels of detail. In this setting, we define ability to perform a task/achieve a goal, and then show that under some reasonable assumptions, if the agent has a strategy by which she is able to achieve a goal at the high level, then we can refine it into a low-level strategy to do so. |
| Researcher Affiliation | Academia | Bita Banihashemi1, Giuseppe De Giacomo2, Yves Lesp erance1 1 York University 2 Sapienza Universit a di Roma bita@cse.yorku.ca, degiacomo@dis.uniroma1.it, lesperan@cse.yorku.ca |
| Pseudocode | No | The paper provides formal definitions of language constructs (e.g., for Con Golog and strategies) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper describes a theoretical framework using Situation Calculus and Con Golog but does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, such as hyperparameters or training configurations. |