Coactive Learning for Locally Optimal Problem Solving
Authors: Robby Goetschalckx, Alan Fern, Prasad Tadepalli
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide an empirical evaluation of the learners in various domains, which show that the Perceptron based algorithms are quite effective and that unlike the case for online classification, the PA algorithms do not yield significant performance gains. |
| Researcher Affiliation | Academia | Robby Goetschalckx, Alan Fern, Prasad Tadepalli School of EECS, Oregon State University, Corvallis, Oregon, USA |
| Pseudocode | Yes | Algorithm 1 Coactive Update(problem xt, black-box solution yt, expert solution y (see text), cost Ct) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | A final experiment was performed using the Yahoo! Learning to Rank dataset (Chapelle and Chang 2011), a real-world dataset consisting of webbased queries and lists of documents which were returned for those queries. |
| Dataset Splits | No | The paper mentions several datasets used for experiments but does not provide specific details on training, validation, or test dataset splits, nor does it specify exact percentages or sample counts for these splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as libraries or solvers with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | The expert can improve this trajectory by looking at three subsequent moves and reordering them, if any such reordering gives an improvement of at least κ = 0.1, until no such improvement is possible.All reported results are averages over 10 runs of the experiments. |