Self Monitoring, Goal Driven Autonomy Agents
Authors: Dustin Dannenhauer
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Previously, we have explored different forms of expectations for anomaly detection in agents operating in Real-Time Strategy (RTS) games, as well as dynamic domains involving planning and execution. In the RTS game Starcraft, we showed that inferred expectations enable high level planning to allow an agent to use more complex plans (e.g. coordinating different types of attacks using multiple groups to attack the enemy) [Dannenhauer and Mu noz-Avila, 2013a; 2015b]. Previously we have explored using a cognitive trace that records data from different cognitive processes (e.g. preception, interpretation, goal selection, planning) and show preliminary results that enable an agent to swap out it s planning faculty for an alternative planner [Cox, 2016]. |
| Researcher Affiliation | Academia | Dustin Dannenhauer Department of Computer Science and Engineering Lehigh University, Bethlehem, PA 18015 dtd212@lehigh.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Figure 1 is a diagram, not pseudocode. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code for the methodology described. |
| Open Datasets | No | The paper mentions working in 'Real-Time Strategy (RTS) games' such as 'Starcraft' and a 'Marsworld domain', but it does not provide concrete access information (link, DOI, repository name, or formal citation with authors/year) for any publicly available or open dataset used in its experiments. It refers to 'Brood War Terrain Analyzer [Perkins, 2010]' which is a tool, not a dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using tools and concepts like 'Brood War Terrain Analyzer' and a 'reasoner' with an 'ontology', but it does not provide specific ancillary software details with version numbers (e.g., 'Python 3.8', 'PyTorch 1.9'). |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings in the main text. |