Belief Manipulation Through Propositional Announcements
Authors: Aaron Hunter, François Schwarzentruber, Eric Tsang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then describe an implemented tool that uses announcement finding to control robot behaviour through belief manipulation. |
| Researcher Affiliation | Academia | Aaron Hunter BC Institute of Technology Burnaby, Canada aaron hunter@bcit.ca Francois Schwarzentruber ENS Rennes Bruz, France francois.schwarzentruber@ens-rennes.fr Eric Tsang BC Institute of Technology Burnaby, Canada surplus.et@gmail.com |
| Pseudocode | Yes | EXIST ANN (K1, . . . , Kn, ψ1, . . . , ψn) 0. Let m be the size of the underlying input vocabulary of K1, . . . , Kn, ψ1, . . . , ψn 1. Guess d1, . . . , dn {0, 1, . . . , m} 2. Guess valuations v1, . . . , vn 3. For all i, j {1, . . . , n} If d(Kj, vi) < dj, reject. 4. For all i {1, . . . , n} If d(Ki, vi) > di, reject. 5. For all i, j {1, . . . , n} If d(Kj, vi) = dj and vi |= ψj, reject. 6. Accept. and FIND ANN DET (K1, . . . , Kn, P1, . . . , Pn, ψ1, . . . , ψn) 0. Let m = maxi,s Pi(s) 1. For each d = (d1, . . . , dn) with 0 di m: 2. For each n-tuple v = (v1, . . . , vn) of valuations: 3. If Pj(vi) < dj for any i, j, continue. 4. If Pi(vi) > di for any i, continue. 5. If Pj(vi) = dj and vi |= ψj for any i, j, continue. 6. Return φ = form( v) 7. Return no solution. |
| Open Source Code | No | Ann B is written in Kotlin4. It is built on the publicly available libraries of Gen B [Hunter and Tsang, 2016]. Briefly, Gen B is a general belief revision solver that is able to calculate the result of any AGM belief revision operation. The paper mentions that their tool Ann B is built on publicly available libraries (Gen B), but it does not provide a link or explicit statement about the source code for Ann B itself being open source or provided. |
| Open Datasets | No | The paper describes a simulated environment for robot control and does not refer to the use of any publicly available or open datasets for training, validation, or testing. The example involves a vocabulary `{patrol, checkgate}` and initial belief states, which are internal to the simulation. |
| Dataset Splits | No | The paper describes a theoretical framework with a simulated demonstrator and does not specify training, validation, or test dataset splits. The problem is framed as a search for a formula, not a machine learning task with data splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments or the simulated robot controller. |
| Software Dependencies | No | Ann B is written in Kotlin4. It is built on the publicly available libraries of Gen B [Hunter and Tsang, 2016]. The footnote states: 'Kotlin is a variant of java, available at www.kotlinlang.org.' While Kotlin and Gen B are mentioned, specific version numbers for either are not provided in the paper. |
| Experiment Setup | No | The paper describes the functionality and algorithm (FIND ANN DET) of the Ann B tool, including how agents and behaviors are defined. However, it does not provide specific numerical hyperparameters (e.g., learning rates, batch sizes) or system-level training configurations typically found in experimental setups, as it focuses on the logic and algorithmic procedure rather than empirical tuning. |