TOMA: Computational Theory of Mind with Abstractions for Hybrid Intelligence
Authors: Emre Erdogan, Frank Dignum, Rineke Verbrugge, Pinar Yolum
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a formalization based on epistemic logic to explain how various inferences enable such a computational theory of mind. Using examples from the medical domain, we demonstrate how having such a theory of mind enables an agent to interact with humans effectively and can increase the quality of the decisions humans make. |
| Researcher Affiliation | Academia | Emre Erdogan EMAIL Utrecht University, Utrecht, Netherlands Frank Dignum EMAIL Ume a University, Ume a, Sweden Rineke Verbrugge EMAIL University of Groningen, Groningen, Netherlands Pınar Yolum EMAIL Utrecht University, Utrecht, Netherlands |
| Pseudocode | No | The paper presents logical derivation tables (e.g., Figure 2, Table 3, 4, 5, 6, 7, 8) to illustrate how an agent uses epistemic logic for abstraction and action decision mechanisms. These tables show structured logical inferences rather than algorithmic pseudocode blocks with step-by-step procedures. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper uses 'examples from the medical domain' to illustrate its theoretical framework. It does not describe or use any specific dataset for empirical evaluation, nor does it provide access information for any dataset. |
| Dataset Splits | No | The paper is theoretical and uses illustrative examples rather than empirical experiments. Therefore, no dataset splits are provided. |
| Hardware Specification | No | The paper focuses on a theoretical framework and uses illustrative examples. It does not describe any experiments that would require specific hardware, and thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper discusses epistemic reasoning tools like 'eclingo (Cabalar et al., 2020), EP-ASP (Son et al., 2017), and other epistemic extensions of Answer Set Programming (Brewka et al., 2011)' as potentially useful for handling basic epistemic reasoning. However, it does not state that these tools (with specific version numbers) were used for the implementation or experimentation of the TOMA framework itself. |
| Experiment Setup | No | The paper describes a theoretical framework and demonstrates its concepts using examples from the medical domain. It does not include any experimental evaluation, and therefore, no experimental setup details, hyperparameters, or training configurations are provided. |