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Found 8 papers, 1 papers with code
On The Expressivity of Recurrent Neural Cascades
A notable implication of our framework is that RNCs can achieve the expressivity of all regular languages by introducing neurons that can implement groups.
The Krohn-Rhodes Logics
In particular, the temporal operators of Past LTL are captured by flip-flops, and they cannot capture any other prime automaton, confining the expressivity within the star-free regular languages.
Automata Cascades: Expressivity and Sample Complexity
Guided by this theory, we propose automata cascades as a structured, modular, way to describe automata as complex systems made of many components, each implementing a specific functionality.
Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision Processes
Our approach combines automata learning and classic reinforcement learning.
Efficient PAC Reinforcement Learning in Regular Decision Processes
Recently regular decision processes have been proposed as a well-behaved form of non-Markov decision process.
The Window Validity Problem in Rule-Based Stream Reasoning
Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data.
Stream Reasoning in Temporal Datalog
In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities.
Improved Answer-Set Programming Encodings for Abstract Argumentation
The design of efficient solutions for abstract argumentation problems is a crucial step towards advanced argumentation systems.
