Association for the Advancement of Artificial Intelligence: AAAI Publications
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Platform-Aware Mission Planning
No full textPlanning for autonomous systems typically requires reasoning with models at different levels of abstraction, and the harmonization of two competing sets of objectives: high-level mission goals that refer to an interaction of the system with the external environment, and low-level platform constraints that aim to preserve the integrity and the correct interaction of the subsystems. The complicated interplay between these two models makes it very hard to reason on the system as a whole, especially when the objective is to find plans with robustness guarantees, considering the non-deterministic behavior of the lower layers of the system.
In this paper, we introduce the problem of Platform-Aware Mission Planning (PAMP), addressing it in the setting of temporal durative actions. The PAMP problem differs from standard temporal planning for its exists-forall nature: the high-level plan dealing with mission goals is required to satisfy safety and executability constraints, for all the possible non-deterministic executions of the low-level model of the platform and the environment. We propose two approaches for solving PAMP. The first baseline approach amalgamates the mission and platform levels, while the second is based on an abstraction-refinement loop that leverages the combination of a planner and a verification engine. We prove the soundness and completeness of the proposed approaches and validate them experimentally, demonstrating the importance of heterogeneous modeling and the superiority of the technique based on abstraction-refinement
On Generating Robust Plans and Linear Execution Strategies in Planning Against Nature
No full textPlanning against nature is a recent concept describing planning and acting in environments in which nature can non-deterministically trigger exogenous events, where the agent has to consider that the state of the environment might change without its consent. Therefore, the agent has to make sure that it eventually achieves its goal (if possible) despite the acts of nature.
In this paper, we leverage the recent concept of robust plans, which assumes that nature might act as an adversary, to design a method for generating linear execution strategies, which assume that nature acts randomly but fairly. In particular, we consider events that have to eventually occur and facts that even if deleted by events will be eventually reachieved by (other) events (because nature acts fairly). To improve the efficiency of both robust plan and linear execution strategy generation methods, we provide an approach allowing us to adopt delete-relaxed heuristics that are used in classical planning
SibylSatOpt: a MaxSAT-based Greedy Optimal Search for TOHTN Planning
No full textThis paper introduces SibylSatOpt, a novel approach to finding optimal plans for Totally-Ordered HTN (TOHTN) problems by leveraging greedy search techniques with MaxSAT. Unlike previous SAT-based HTN planners that employed a blind breadth-first search strategy, SibylSatOpt is guided by an admissible heuristic. This heuristic combines a relaxed MaxSAT encoding of the problem with the Task Decomposition Graph (TDG) heuristic. As we demonstrate, the admissibility of the heuristic guarantees that the found solution is optimal. Experimental results on IPC benchmarks show that SibylSatOpt significantly outperforms existing optimal TOHTN planners in both runtime and problem coverage
Knowledge Engineering for Planning and Scheduling in the LLM Era
No full textAutomated planning requires explicit domain knowledge, typically represented in PDDL, to generate effective solutions. The process of formulating, maintaining, and validating this knowledge is the cornerstone of Knowledge Engineering for Planning and Scheduling (KEPS). Although Large Language Models (LLMs) have shown promise for automated planning tasks, and are gaining popularity in the field, their impact on KEPS remains unexplored.
In this paper we investigate the potential of LLMs to streamline and enhance the KEPS field, by taking a close look at the processes used to develop explicit symbolic knowledge models in safety-related applications. The paper's findings are that while LLMs can assist in knowledge acquisition and formulation, human domain expertise and external symbolic validators remain indispensable for ensuring correctness, operationality and completeness of planning applications
Minimizing Fuel in Multi-Agent Pathfinding
No full textThe multi-agent pathfinding problem (MAPF) of finding conflict-free paths for multiple agents has attracted a large number of researchers in the past. The cost of the solution is commonly measured by the sum-of-costs (SOC) cost function or, less commonly, by Makespan. In this paper, we focus on the Fuel cost function, which is the number of physical steps the agents traverse. While Fuel was mentioned in many previous papers, our paper is the first to deepen into it. We introduce an A*-based algorithm and a CBS-based algorithm for Fuel. We study Fuel theoretically, showing that it can be (perhaps non-intuitively) more complex than SOC. Finally, we experimentally compare both algorithms against each other and against their SOC counter parts, studying their advantages and disadvantages
Bi-Objective Search for the Traveling Salesman Problem with Time Windows and Vacant Penalties
No full textThis paper investigates a Traveling Salesman Problem with Time Windows and Vacant Penalties (TSP-TW-VP), which plans a path to service a set of machines at different locations within their respective time windows while minimizing two objective functions: the finish time and penalty for machine vacancy. There is often no single solution that optimizes both objectives simultaneously, and the problem thus seeks the Pareto-optimal solutions. TSP-TW-VP generalizes TSP-TW and is therefore NP-hard. To solve the problem, this paper develops an algorithm called Search with Look-Ahead Pruning (S-LAP) that is guaranteed to find all Pareto-optimal solutions for TSP-TW-VP. S-LAP gains computational efficiency by introducing a novel look-ahead pruning rule, and a fast dominance checking method based on both the objective functions and path history. Experimental results show that the proposed look-ahead pruning and fast dominance can speed up the search for 2-8 times over 4 different datasets
Multi-armed Bandit Algorithms for the Boolean Satisfiability Problem: A Survey
No full textThis paper provides a survey of recent literature on the use of multi-armed bandit algorithms to solve the Boolean satisfiability problem (SAT), a well-known NP-complete problem with broad applications in academia and industry. The application of bandit algorithms in modern SAT solvers has achieved great success in recent years, as evidenced by the excellent performance of SAT solvers using bandit algorithms in SAT competitions. Bandit algorithms are classic randomized optimization algorithms that strike a balance between exploration and exploitation and can aid in designing and improving heuristics in SAT solvers. In this paper, we introduce several aspects of the application of bandit algorithms in modern SAT solvers, ranging from heuristic methods in CDCL and SLS solvers to strategies in parallel SAT solvers. The use of bandit algorithms in SAT solvers still holds great potential. In conclusion of the survey, we summarize the current issues and suggest possible future research directions
A Framework for Integrating Privacy by Design into Generative AI Applications
No full textGenerative AI applications rely on vast amounts of data, raising significant privacy concerns. Traditional privacy safeguards often follow a reactive approach, addressing risks only after deployment. However, given the evolving nature of AI-driven data processing, a proactive and systematic approach to privacy integration is necessary. This paper presents a framework for embedding principles of Privacy by Design (PbD) and other privacy mechanisms throughout the AI lifecycle. Unlike traditional PbD implementations that primarily focus on data collection and storage, the proposed framework intro-duces privacy-preserving techniques at the model level, ensuring AI models minimize data exposure during training and inference. We propose dynamic user con-sent mechanisms, differential privacy-enhanced model architectures, federated learning for decentralized training, and real-time privacy risk monitoring tools to enhance transparency, security, and user control. Additionally, the framework incorporates fairness-aware privacy techniques, ensuring that privacy measures do not exacerbate bias in AI models. The framework is evaluated through empirical testing of privacy leakage risks and differential privacy tradeoff analysis. Results demonstrate that integrating PbD like mechanisms into generative AI enhances privacy protections while maintaining AI utility and regulatory compliance
Explainability-Driven Defense: Grad-CAM-Guided Model Refinement Against Adversarial Threats
No full textDeep learning models have excelled in tasks like image recognition and autonomous systems but remain vulnerable to adversarial attacks and spurious correlations, limiting their reliability in real-world and safety-critical settings. To address these challenges, we propose a novel framework that leverages explainable Artificial Intelligence (XAI) to enhance the robustness of Convolutional Neural Networks. Our approach integrates Grad-CAM insights into the model refinement process, guiding feature masking to reduce reliance on irrelevant or misleading features. We introduce three masking strategies: (1) binary masking to retain high-activation regions, (2) Gaussian-blurred masking to preserve contextual information while reducing noise, and (3) difference-based masking to remove unstable features unique to the baseline model. We evaluate these strategies against two common adversarial attack methods—Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Results show that all three strategies improve FGSM accuracy, with binary and difference-based masking providing consistent gains across perturbation levels. Gaussian-blurred masking delivers the highest improvement in PGD accuracy, particularly at higher perturbation strengths
Unsupervised Machine Learning Using Cerebrospinal Fluid Proteomics for Understanding Parkinson’s Disease Progression
No full textThis study explores the potential of advanced, context-aware machine learning algorithms, such as autoencoders, to represent longitudinal cerebrospinal fluid proteomic data, enabling the objective discovery of two patient strata with significance