Automata Learning from Preference and Equivalence Queries

• URL: http://arxiv.org/abs/2308.09301v2
• Date: Tue, 19 Nov 2024 23:57:24 GMT
• Title: Automata Learning from Preference and Equivalence Queries
• Authors: Eric Hsiung, Joydeep Biswas, Swarat Chaudhuri,
• Abstract summary: We propose a novel variant of the active automata learning problem: actively learn finite automata using preference queries. ReMAP is guaranteed to correctly infer the minimal complexity with query complexity under exact equivalence queries. Our empirical evaluations indicate REMAP scales to large automata is effective at learning correct automata from consistent teachers.
• Score: 17.33092604696224
• License:
• Abstract: Active automata learning from membership and equivalence queries is a foundational problem with numerous applications. We propose a novel variant of the active automata learning problem: actively learn finite automata using preference queries -- i.e., queries about the relative position of two sequences in a total order -- instead of membership queries. Our solution is REMAP, a novel algorithm which leverages a symbolic observation table along with unification and constraint solving to navigate a space of symbolic hypotheses (each representing a set of automata), and uses satisfiability-solving to construct a concrete automaton from a symbolic hypothesis. REMAP is guaranteed to correctly infer the minimal automaton with polynomial query complexity under exact equivalence queries, and achieves PAC-identification ($\varepsilon$-approximate, with high probability) of the minimal automaton using sampling-based equivalence queries. Our empirical evaluations of REMAP on the task of learning reward machines for two reinforcement learning domains indicate REMAP scales to large automata and is effective at learning correct automata from consistent teachers, under both exact and sampling-based equivalence queries.

Related papers

• Learning Quantitative Automata Modulo Theories [17.33092604696224]
  We present QUINTIC, an active learning algorithm, wherein the learner infers a valid automaton through deductive reasoning. Our evaluations utilize theory of rationals in order to learn summation, discounted summation, product, and classification quantitative automata.
  arXiv  Detail & Related papers  (2024-11-15T21:51:14Z)
• LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning [11.037017229299607]
  The emergence of intelligence in large language models (LLMs) has inspired investigations into their integration into automata learning. This paper introduces the probabilistic Minimally Adequate Teacher (pMAT) formulation. We develop techniques to improve answer accuracy and ensure the correctness of the learned automata.
  arXiv  Detail & Related papers  (2024-08-06T07:12:09Z)
• Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars [66.823588073584]
  Large language models (LLMs) have shown impressive capabilities in real-world applications. The quality of these exemplars in the prompt greatly impacts performance. Existing methods fail to adequately account for the impact of exemplar ordering on the performance.
  arXiv  Detail & Related papers  (2024-05-25T08:23:05Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• Learning Hidden Markov Models Using Conditional Samples [72.20944611510198]
  This paper is concerned with the computational complexity of learning the Hidden Markov Model (HMM) In this paper, we consider an interactive access model, in which the algorithm can query for samples from the conditional distributions of the HMMs. Specifically, we obtain efficient algorithms for learning HMMs in settings where we have query access to the exact conditional probabilities.
  arXiv  Detail & Related papers  (2023-02-28T16:53:41Z)
• Unified Functional Hashing in Automatic Machine Learning [58.77232199682271]
  We show that large efficiency gains can be obtained by employing a fast unified functional hash. Our hash is "functional" in that it identifies equivalent candidates even if they were represented or coded differently. We show dramatic improvements on multiple AutoML domains, including neural architecture search and algorithm discovery.
  arXiv  Detail & Related papers  (2023-02-10T18:50:37Z)
• Induction and Exploitation of Subgoal Automata for Reinforcement Learning [75.55324974788475]
  We present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induction of a subgoal automaton, an automaton whose edges are labeled by the task's subgoals. A subgoal automaton also consists of two special states: a state indicating the successful completion of the task, and a state indicating that the task has finished without succeeding.
  arXiv  Detail & Related papers  (2020-09-08T16:42:55Z)
• Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs [62.71505254770827]
  We propose a conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts. Our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
  arXiv  Detail & Related papers  (2020-05-28T08:26:06Z)

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.