A Simplicial Model for $KB4_n$: Epistemic Logic with Agents that May Die
- URL: http://arxiv.org/abs/2108.10293v1
- Date: Mon, 23 Aug 2021 17:10:13 GMT
- Title: A Simplicial Model for $KB4_n$: Epistemic Logic with Agents that May Die
- Authors: Eric Goubault and J\'er\'emy Ledent and Sergio Rajsbaum
- Abstract summary: This one dimensional structure contains implicit higher-dimensional information beyond pairwise interactions.
We extend the theory to encompass all simplicial models - including the ones that are not pure.
- Score: 2.2946354246664558
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The standard semantics of multi-agent epistemic logic $S5$ is based on Kripke
models whose accessibility relations are reflexive, symmetric and transitive.
This one dimensional structure contains implicit higher-dimensional information
beyond pairwise interactions, that has been formalized as pure simplicial
models in previous work from the authors. Here we extend the theory to
encompass all simplicial models - including the ones that are not pure. The
corresponding Kripke models are those where the accessibility relation is
symmetric and transitive, but might not be reflexive. This yields the epistemic
logic $KB4$ which can reason about situations where some of the agents may die.
Related papers
- On the Emergence of Linear Analogies in Word Embeddings [5.440589713820591]
Models such as Word2Vec and GloVe construct word embeddings based on the co-occurrence probability $P(i,j)$ of words $i$ and $j$ in text corpora.<n>We introduce a theoretical generative model in which words are defined by binary semantic attributes, and co-occurrence probabilities are derived from attribute-based interactions.
arXiv Detail & Related papers (2025-05-24T11:42:26Z) - Interpretable Neural Causal Models with TRAM-DAGs [0.0]
We bridge the gap between interpretability and flexibility in causal modeling with TRAM-DAG.
We show that TRAM-DAGs are interpretable but also achieve equal or superior performance in queries ranging from $L_3$ to $L_1$ in the causal hierarchy.
For the continuous case, TRAM-DAGs allow for counterfactual queries for three common causal structures, including unobserved confounding.
arXiv Detail & Related papers (2025-03-20T14:51:04Z) - Interaction Asymmetry: A General Principle for Learning Composable Abstractions [27.749478197803256]
We show that interaction asymmetry enables both disentanglement and compositional generalization.
We propose an implementation of these criteria using a flexible Transformer-based VAE, with a novel regularizer on the attention weights of the decoder.
arXiv Detail & Related papers (2024-11-12T13:33:26Z) - Simplicial Models for the Epistemic Logic of Faulty Agents [1.474723404975345]
We show that subtle design choices in the definition of impure simplicial models can result in different axioms of the resulting logic.
We illustrate them via distributed computing examples of synchronous systems where processes may crash.
arXiv Detail & Related papers (2023-11-02T16:00:28Z) - Meaning Representations from Trajectories in Autoregressive Models [106.63181745054571]
We propose to extract meaning representations from autoregressive language models by considering the distribution of all possible trajectories extending an input text.
This strategy is prompt-free, does not require fine-tuning, and is applicable to any pre-trained autoregressive model.
We empirically show that the representations obtained from large models align well with human annotations, outperform other zero-shot and prompt-free methods on semantic similarity tasks, and can be used to solve more complex entailment and containment tasks that standard embeddings cannot handle.
arXiv Detail & Related papers (2023-10-23T04:35:58Z) - Evaluating the Robustness of Interpretability Methods through
Explanation Invariance and Equivariance [72.50214227616728]
Interpretability methods are valuable only if their explanations faithfully describe the explained model.
We consider neural networks whose predictions are invariant under a specific symmetry group.
arXiv Detail & Related papers (2023-04-13T17:59:03Z) - Misspecification in Inverse Reinforcement Learning [80.91536434292328]
The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function $R$ from a policy $pi$.
One of the primary motivations behind IRL is to infer human preferences from human behaviour.
This means that they are misspecified, which raises the worry that they might lead to unsound inferences if applied to real-world data.
arXiv Detail & Related papers (2022-12-06T18:21:47Z) - KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal [70.15267479220691]
We consider and analyze the sample complexity of model reinforcement learning with a generative variance-free model.
Our analysis shows that it is nearly minimax-optimal for finding an $varepsilon$-optimal policy when $varepsilon$ is sufficiently small.
arXiv Detail & Related papers (2022-05-27T19:39:24Z) - On the Equivalence of Causal Models: A Category-Theoretic Approach [0.0]
We develop a criterion for determining the equivalence of causal models having different but homomorphic directed acyclic graphs over discrete variables.
The equivalence of causal models is then defined in terms of a natural transformation or isomorphism between two such functors.
It is shown that when one model is a $Phi$-abstraction of another, the intervention of the former can be consistently translated into that of the latter.
arXiv Detail & Related papers (2022-01-18T13:43:06Z) - Validation and Inference of Agent Based Models [0.0]
Agent Based Modelling (ABM) is a computational framework for simulating the behaviours and interactions of autonomous agents.
Recent research in ABC has yielded increasingly efficient algorithms for calculating the approximate likelihood.
These are investigated and compared using a pedestrian model in the Hamilton CBD.
arXiv Detail & Related papers (2021-07-08T05:53:37Z) - On the Generative Utility of Cyclic Conditionals [103.1624347008042]
We study whether and how can we model a joint distribution $p(x,z)$ using two conditional models $p(x|z)$ that form a cycle.
We propose the CyGen framework for cyclic-conditional generative modeling, including methods to enforce compatibility and use the determined distribution to fit and generate data.
arXiv Detail & Related papers (2021-06-30T10:23:45Z) - On Exploiting Hitting Sets for Model Reconciliation [53.81101846598925]
In human-aware planning, a planning agent may need to provide an explanation to a human user on why its plan is optimal.
A popular approach to do this is called model reconciliation, where the agent tries to reconcile the differences in its model and the human's model.
We present a logic-based framework for model reconciliation that extends beyond the realm of planning.
arXiv Detail & Related papers (2020-12-16T21:25:53Z) - Model Interpretability through the Lens of Computational Complexity [1.6631602844999724]
We study whether folklore interpretability claims have a correlate in terms of computational complexity theory.
We show that both linear and tree-based models are strictly more interpretable than neural networks.
arXiv Detail & Related papers (2020-10-23T09:50:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.