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We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.

This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs.

Logic-based event recognition systems infer occurrences of events in time using a set of event definitions in the form of first-order rules.

ACTIVITY RECOGNITION INDUCTIVE LOGIC PROGRAMMING TEMPORAL LOGIC

The Event Calculus is a temporal logic that has been used as a basis in event recognition applications, providing among others, direct connections to machine learning, via Inductive Logic Programming (ILP).

ACTIVITY RECOGNITION INDUCTIVE LOGIC PROGRAMMING TEMPORAL LOGIC

We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP).

In the context of multi-agent systems, the rational verification problem is concerned with checking which temporal logic properties will hold in a system when its constituent agents are assumed to behave rationally and strategically in pursuit of individual objectives.

We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics.

Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics, and biology.

DECISION MAKING DECISION MAKING UNDER UNCERTAINTY HIERARCHICAL REINFORCEMENT LEARNING SAFE REINFORCEMENT LEARNING TEMPORAL LOGIC

This probability (certificate) is also calculated in parallel with policy learning when the state space of the MDP is finite: as such, the RL algorithm produces a policy that is certified with respect to the property.

DECISION MAKING DECISION MAKING UNDER UNCERTAINTY HIERARCHICAL REINFORCEMENT LEARNING SAFE REINFORCEMENT LEARNING TEMPORAL LOGIC

With this reward function, the policy synthesis procedure is "constrained" by the given specification.

DECISION MAKING DECISION MAKING UNDER UNCERTAINTY HIERARCHICAL REINFORCEMENT LEARNING SAFE REINFORCEMENT LEARNING TEMPORAL LOGIC