Annals of Mathematics and Artificial Intelligence最新文献

In this paper, a novel method is presented to increase the developability of the quasi-developable Bézier surface from two design curves using the Simulated Annealing-based Shape Parameter Search (SASPS) algorithm based on the shape parameters of the cubic Bézier design curves. In addition, another new method for determining the number of sampling points is given to increase the developability degree. It allows perturbing the design curves within the limits allowed by the user based on the shape parameters. We also defined a multi-objective function for the optimal quasi-developable Bézier surface. Example models show that the proposed method is efficient and effective in achieving optimal developability.

Robustness is a crucial requirement for the deployment of AI systems in real-world scenarios. In the context of AI planning, the concept of action reversibility, i.e., the ability to undo the effects of an action using a reverse plan, is a promising direction for achieving robust plans. Plans composed exclusively of reversible actions exhibit resilience against goal changes during the execution of the plan. However, the evaluation of action reversibility systems in STRIPS planning presents a challenge, given that standard planning benchmarks are often not suitable. Early experiments using a naive implementation of an action reversibility algorithm show that the available domain generation approach is susceptible to bias. Building on this existing domain generator, we introduce two slight variations that exhibit entirely different search space characteristics. We assess these domain generators using the naive action reversibility implementation and existing ASP implementations, and demonstrate that different generators indeed favor different implementations. As a follow-up to this line of research, we present a generalized domain generator facilitating the creation of domains with diverse search space characteristics. To finally reduce the utilization of contrived generation patterns, we propose another domain generator based on the Barabási-Albert model yielding less rigid domains. Our experiments demonstrate that these new domain generators can produce a variety of domains with diverse search space characteristics, enabling a less biased evaluation of action reversibility systems.

This paper presents a performance evaluation of several heuristic search algorithms in the context of pathfinding. Our objective is to assess the performance of these algorithms in various grid-based environments to present how specific domain features influence their efficiency. Additionally, we extend our experiments by incorporating Multi-Agent Path Finding (MAPF) benchmarks, using handcrafted features and features extracted with Convolutional Neural Network (CNN) to characterize the maps. The results of our evaluation were later used to train machine learning models capable of predicting the efficient algorithm for a given pathfinding task based on performance criteria. This multi-algorithm pathfinding method enhances the selection of the best algorithm for different pathfinding problems. Furthermore, we revealed the most important features that impact the selection of the efficient algorithm. We identify the most important characteristics of the grid that affect the selection and performance of the algorithms.

Forgetting variables from a propositional formula may increase its size. Introducing new variables is a way to shorten it. Both operations can be expressed in terms of common equivalence, a weakened version of equivalence. In turn, common equivalence can be expressed in terms of forgetting. An algorithm for forgetting and checking common equivalence in polynomial space is given for the Horn case; it is polynomial-time for the subclass of single-head formulae. Minimizing after forgetting is polynomial-time if the formula is also acyclic and variables cannot be introduced, NP-hard when they can.

Interactive reinforcement learning is an effective way to train agents via human feedback. However, it often requires the trainer (a human who provides feedback to the agent) to know the correct action for the agent. If the trainer is not always reliable, the wrong feedback may hinder the agent’s training. In addition, there is no consensus on the best form of human feedback in interactive reinforcement learning. To address these problems, in this paper, we explore the performance of binary reward as the reward form. Moreover, we propose a novel interactive reinforcement learning system called Multi-Trainer Interactive Reinforcement Learning (MTIRL), which can aggregate binary feedback from multiple imperfect trainers into a reliable reward for agent training in a reward-sparse environment. In addition, the review model in MTIRL can correct the unreliable rewards. In particular, our experiments for evaluating reward forms show that binary reward outperforms other reward forms, including ranking reward, scaling reward, and state value reward. In addition, our question-answer experiments show that our aggregation method outperforms the state-of-the-art aggregation methods, including majority voting, weighted voting, and the Bayesian aggregation method. Finally, we conduct grid-world experiments to show that the policy trained by the MTIRL with the review model is closer to the optimal policy than that without a review model.

This article introduces a new decision tree algorithm that accounts for time-varying covariates in the decision-making process. Traditional decision tree algorithms assume that the covariates are static and do not change over time, which can lead to inaccurate predictions in dynamic environments. Other existing methods suggest workaround solutions such as the pseudo-subject approach, discussed in the article. The proposed algorithm utilises a different structure and a time-penalised splitting criterion that allows a recursive partitioning of both the covariates space and time. Relevant historical trends are then inherently involved in the construction of a tree, and are visible and interpretable once it is fit. This approach allows for innovative and highly interpretable analysis in settings where the covariates are subject to change over time. The effectiveness of the algorithm is demonstrated through a real-world data application in life insurance. The results presented in this article can be seen as an introduction or proof-of-concept of our time-penalised approach, and the algorithm’s theoretical properties and comparison against existing approaches on datasets from various fields, including healthcare, finance, insurance, environmental monitoring, and data mining in general, will be explored in forthcoming work.

Contests are commonly used as a mechanism for eliciting effort and participation in multi-agent settings. Naturally, and much like with various other mechanisms, the information provided to the agents prior to and throughout the contest fundamentally influences its outcomes. In this paper we study the problem of information providing whenever the contest organizer does not initially hold the information and obtaining it is potentially costly. As the underlying contest mechanism for our model we use the binary contest, where contestants’ strategy is captured by their decision whether or not to participate in the contest in the first place. Here, it is often the case that the contest organizer can proactively obtain and provide contestants information related to their expected performance in the contest. We provide a comprehensive equilibrium analysis of the model, showing that even when such information is costless, it is not necessarily the case that the contest organizer will prefer to obtain and provide it to all agents, let alone when the information is costly.

Ensuring the safe and effective operation of autonomous systems is a complex undertaking that inherently relies on underlying decision-making processes. To rigorously analyze these processes, formal verification methods, such as model checking, offer a valuable means. However, the non-deterministic nature of realistic environments makes these approaches challenging and often impractical. This work explores the capabilities of a constraint-based planning approach, Tumato, in generating policies that guide the system to predefined goals while adhering to safety constraints. Constraint-based planning approaches are inherently able to provide guarantees of soundness and completeness. Our primary contribution lies in extending Tumato’s capabilities to accommodate non-deterministic outcomes of actions, enhancing the robustness of the behavior. Originally designed to accommodate only deterministic outcomes, actions can now be modeled to include alternative outcomes to address contingencies explicitly. The adapted solver generates policies that enable reaching the goals in a safe manner, even when such alternative outcomes of actions occur. Additionally, we introduce a purely declarative manner for specifying safety in Tumato to further enhance its expressiveness as well as to reduce the susceptibility to errors during specification. The incorporation of cost or duration values to actions enables the solver to restore safety in the most preferred manner when necessary. Finally, we highlight the overlap of Tumato’s safety-related capabilities with a systems-theoretic approach, STPA (Systems-Theoretic Process Analysis). The aim is to emphasize the ability to avoid unsafe control actions without their explicit identification, contributing to a more comprehensive and holistic understanding of safety.