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Found 9 papers, 3 papers with code
AutoPlanBench: Automatically generating benchmarks for LLM planners from PDDL
LLMs are being increasingly used for planning-style tasks, but their capabilities for planning and reasoning are poorly understood.
Specifying and Testing $k$-Safety Properties for Machine-Learning Models
Here, we show the wide applicability of $k$-safety properties for machine-learning models and present the first specification language for expressing them.
Expressivity of Planning with Horn Description Logic Ontologies (Technical Report)
State constraints in AI Planning globally restrict the legal environment states.
An Explainable AI System for the Diagnosis of High Dimensional Biomedical Data
A comparison to a selection of state of the art explainable AI systems shows that ALPODS operates efficiently on known benchmark data and also on everyday routine case data.
Iterative Planning with Plan-Space Explanations: A Tool and User Study
Adopting the recent approach to answer such questions in terms of plan-property dependencies, here we implement a tool and user interface for human-guided iterative planning including plan-space explanations.
Generating Instructions at Different Levels of Abstraction
When generating technical instructions, it is often convenient to describe complex objects in the world at different levels of abstraction.
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version
Our evaluations show that for this sequential decision making problem, deep reinforcement learning performs best in many aspects even though for imitation learning optimal decisions are considered.
Towards Automated Network Mitigation Analysis (extended)
Penetration testing is a well-established practical concept for the identification of potentially exploitable security weaknesses and an important component of a security audit.
Message-Based Web Service Composition, Integrity Constraints, and Planning under Uncertainty: A New Connection
The special case, which we term "forward effects", is characterized by the fact that every ramification of a web service application involves at least one new constant generated as output by the web service.
