Learning Moore Machines from Input-Output Traces

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper we study this problem for finite state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We also compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers, and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample

Language and Automata Theory and Applications

International audienc

Interpretable Sequence Classification via Discrete Optimization

Sequence classification is the task of predicting a class label given a sequence of observations. In many applications such as healthcare monitoring or intrusion detection, early classification is crucial to prompt intervention. In this work, we learn sequence classifiers that favour early classification from an evolving observation trace. While many state-of-the-art sequence classifiers are neural networks, and in particular LSTMs, our classifiers take the form of finite state automata and are learned via discrete optimization. Our automata-based classifiers are interpretable---supporting explanation, counterfactual reasoning, and human-in-the-loop modification---and have strong empirical performance. Experiments over a suite of goal recognition and behaviour classification datasets show our learned automata-based classifiers to have comparable test performance to LSTM-based classifiers, with the added advantage of being interpretable

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Discovering logical knowledge in non-symbolic domains

Deep learning and symbolic artificial intelligence remain the two main paradigms in Artificial Intelligence (AI), each presenting their own strengths and weaknesses. Artificial agents should integrate both of these aspects of AI in order to show general intelligence and solve complex problems in real-world scenarios; similarly to how humans use both the analytical left side and the intuitive right side of their brain in their lives. However, one of the main obstacles hindering this integration is the Symbol Grounding Problem [144], which is the capacity to map physical world observations to a set of symbols. In this thesis, we combine symbolic reasoning and deep learning in order to better represent and reason with abstract knowledge. In particular, we focus on solving non-symbolic-state Reinforcement Learning environments using a symbolic logical domain. We consider different configurations: (i) unknown knowledge of both the symbol grounding function and the symbolic logical domain, (ii) unknown knowledge of the symbol grounding function and prior knowledge of the domain, (iii) imperfect knowledge of the symbols grounding function and unknown knowledge of the domain. We develop algorithms and neural network architectures that are general enough to be applied to different kinds of environments, which we test on both continuous-state control problems and image-based environments. Specifically, we develop two kinds of architectures: one for Markovian RL tasks and one for non-Markovian RL domains. The first is based on model-based RL and representation learning, and is inspired by the substantial prior work in state abstraction for RL [115]. The second is mainly based on recurrent neural networks and continuous relaxations of temporal logic domains. In particular, the first approach extracts a symbolic STRIPS-like abstraction for control problems. For the second approach, we explore connections between recurrent neural networks and finite state machines, and we define Visual Reward Machines, an extension to non-symbolic domains of Reward Machines [27], which are a popular approach to non-Markovian RL tasks