Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
How to Do Things with Words: A Bayesian Approach
Authors: Piotr Gmytrasiewicz
JAIR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We treat communication as a type of action and therefore, decisions regarding communicative acts are based on decision-theoretic planning using the Bellman optimality principle and value iteration, just as they are for all other rational actions. As in any form of planning, the results of actions need to be precisely specified. We use the Bayes theorem to derive how agents update their beliefs in CIPOMDPs; updates are due to agents actions, observations, messages they send to other agents, and messages they receive from others. The Bayesian decision-theoretic approach frees us from the commonly made assumption of cooperative discourse we consider agents which are free to be dishonest while communicating and are guided only by their selfish rationality. We use a simple Tiger game to illustrate the belief update, and to show that the ability to rationally communicate allows agents to improve efficiency of their interactions. |
| Researcher Affiliation | Academia | Piotr Gmytrasiewicz EMAIL Computer Science Department and AI Laboratory, University of Illinois at Chicago, Chicago, Illinois, 60607, USA |
| Pseudocode | No | The paper includes mathematical formulations, propositions, and theorems (e.g., Proposition 1, Bellman equation (8), Theorem 1, Theorem 2) but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide links to any code repositories. |
| Open Datasets | No | The paper uses the "tiger game (Kaelbling et al., 1998)" as an illustrative example. This refers to a problem environment, not a dataset in the sense of collected empirical data. The parameters for this game are described within the paper (e.g., Reward Function in Table 1), but no access information (link, DOI, repository, or specific citation with authors/year for a dataset) is provided. |
| Dataset Splits | No | The paper describes a theoretical framework and illustrates it using a game environment. It does not use any empirical datasets that would require training, validation, or test splits. |
| Hardware Specification | No | The paper focuses on a theoretical framework and its illustration using a game environment. It does not mention any specific hardware (e.g., CPU, GPU models, memory, or computational resources) used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any implementation or experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the parameters of the Tiger game environment (e.g., reward function in Table 1, observation accuracy) for its illustration. However, it does not provide details about an experimental setup in terms of hyperparameters, model initialization, training schedules, or other system-level settings typically found in empirical studies. |