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Found 28 papers, 2 papers with code
Composition of Nondeterministic and Stochastic Services for LTLf Task Specifications
In this paper, we study the composition of services so as to obtain runs satisfying a task specification in Linear Temporal Logic on finite traces (LTLf).
Symbolic LTLf Best-Effort Synthesis
We consider an agent acting to fulfil tasks in a nondeterministic environment.
LTLf Synthesis Under Environment Specifications for Reachability and Safety Properties
In this paper, we study LTLf synthesis under environment specifications for arbitrary reachability and safety properties.
LTLf Best-Effort Synthesis in Nondeterministic Planning Domains
We study best-effort strategies (aka plans) in fully observable nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on Finite Traces (LTLf).
Temporally Extended Goal Recognition in Fully Observable Non-Deterministic Domain Models
We develop the first approach capable of recognizing goals in such settings and evaluate it using different LTLf and PLTLf goals over six FOND planning domain models.
Abstraction of Nondeterministic Situation Calculus Action Theories -- Extended Version
We develop a general framework for abstracting the behavior of an agent that operates in a nondeterministic domain, i. e., where the agent does not control the outcome of the nondeterministic actions, based on the nondeterministic situation calculus and the ConGolog programming language.
Exploiting Multiple Abstractions in Episodic RL via Reward Shaping
One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains is the large number of samples required to learn an optimal policy.
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.
Mimicking Behaviors in Separated Domains
Devising a strategy to make a system mimicking behaviors from another system is a problem that naturally arises in many areas of Computer Science.
Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision Processes
Our approach combines automata learning and classic reinforcement learning.
Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic: A Polynomial Reduction to Standard Planning
We study temporally extended goals expressed in Pure-Past LTL (PPLTL).
Iterative Depth-First Search for Fully Observable Non-Deterministic Planning
However, most of the existing algorithms are not robust for dealing with both non-determinism and task size.
AI-Augmented Business Process Management Systems: A Research Manifesto
AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology.
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.
Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models
Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of possible goals, a domain model, and a sequence of observations as a sample of the plan being executed in the environment.
Behavioral QLTL
Behavioral QLTL is characterized by the fact that the functions that assign the truth value of the quantified propositions along the trace can only depend on the past.
Logic in Computer Science
Stochastic Fairness and Language-Theoretic Fairness in Planning on Nondeterministic Domains
This important difference has been overlooked in the planning literature, and we argue has led to confusion in a number of published algorithms which use reductions that were stated for state-action fairness, for which they are incorrect, while being correct for stochastic fairness.
LTLf Synthesis with Fairness and Stability Assumptions
A key observation here is that even if we consider systems with LTLf goals on finite traces, environment assumptions need to be expressed over infinite traces, since accomplishing the agent goals may require an unbounded number of environment action.
Generalized Planning: Non-Deterministic Abstractions and Trajectory Constraints
Moreover, for a broad class of problems that involve integer variables that can be increased or decreased, trajectory constraints can be compiled away, reducing generalized planning to fully observable non-deterministic planning.
Planning and Synthesis Under Assumptions
In Reasoning about Action and Planning, one synthesizes the agent plan by taking advantage of the assumption on how the environment works (that is, one exploits the environment's effects, its fairness, its trajectory constraints).
Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf restraining specifications
In this work we investigate on the concept of "restraining bolt", envisioned in Science Fiction.
Situation Calculus for Synthesis of Manufacturing Controllers
Manufacturing is transitioning from a mass production model to a manufacturing as a service model in which manufacturing facilities 'bid' to produce products.
Hybrid Temporal Situation Calculus
The ability to model continuous change in Reiter's temporal situation calculus action theories has attracted a lot of interest.
Specifying Non-Markovian Rewards in MDPs Using LDL on Finite Traces (Preliminary Version)
In Markov Decision Processes (MDPs), the reward obtained in a state depends on the properties of the last state and action.
Bounded Situation Calculus Action Theories
A bounded action theory is one which entails that, in every situation, the number of object tuples in the extension of fluents is bounded by a given constant, although such extensions are in general different across the infinitely many situations.
LTLf and LDLf Monitoring: A Technical Report
LDLf is a powerful logic that captures all monadic second order logic on finite traces, which is obtained by combining regular expressions and LTLf, adopting the syntax of propositional dynamic logic (PDL).
Description Logic Knowledge and Action Bases
Description logic Knowledge and Action Bases (KAB) are a mechanism for providing both a semantically rich representation of the information on the domain of interest in terms of a description logic knowledge base and actions to change such information over time, possibly introducing new objects.
