Association for the Advancement of Artificial Intelligence: AAAI Publications
Not a member yet
26155 research outputs found
Sort by
Recursive Aggregates as Intensional Functions in Answer Set Programming: Semantics and Strong Equivalence
No full textThis paper shows that the semantics of programs with aggregates implemented by the solvers clingo and dlv can be characterized as extended First-Order formulas with intensional functions in the logic of Here-and-There. Furthermore, this characterization can be used to study the strong equivalence of programs with aggregates under either semantics. We also present a transformation that reduces the task of checking strong equivalence to reasoning in classical First-Order logic, which serves as a foundation for automating this procedure
Temporal Causal Reasoning with (Non-Recursive) Structural Equation Models
No full textStructural equation models (SEM) are a standard approach to representing causal dependencies between variables. In this paper we propose a new interpretation of existing formalisms in the field of Actual Causality in which SEM's are viewed as mechanisms transforming the dynamics of exogenous variables into the dynamics of endogenous variables. This allows us to combine counterfactual causal reasoning with existing temporal logic formalizms, and to introduce a temporal logic, CPLTL, for causal reasoning about such structures. Then, we demonstrate that the standard restriction to so-called recursive models (with no cycles in the dependency graphs) is not necessary in our approach. This fact provides us extra tools for reasoning about mutually dependent processes and feedback loops. Finally, we introduce the notions of model equivalence for temporal causal models and show that CPLTL has an efficient model-checking procedure
A Variable Occurrence-Centric Framework for Inconsistency Handling
No full textIn this paper, we introduce a syntactic framework for analyzing and handling inconsistencies in propositional bases. Our approach focuses on examining the relationships between variable occurrences within conflicts. We propose two dual concepts: Minimal Inconsistency Relation (MIR) and Maximal Consistency Relation (MCR). Each MIR is a minimal equivalence relation on variable occurrences that results in inconsistency, while each MCR is a maximal equivalence relation designed to prevent inconsistency. Notably, MIRs capture conflicts overlooked by minimal inconsistent subsets. Using MCRs, we develop a series of non-explosive inference relations. The main strategy involves restoring consistency by modifying the propositional base according to each MCR, followed by employing the classical inference relation to derive conclusions. Additionally, we propose an unusual semantics that assigns truth values to variable occurrences instead of the variables themselves. The associated inference relations are established through Boolean interpretations compatible with the occurrence-based models
Temporal Conjunctive Query Answering via Rewriting
No full textQuerying temporal data has recently gained traction in several artificial intelligence applications. As operational domains of intelligent agents are constantly being expanded, there is a strong need for representing domain knowledge. This comes in the form of ontologies, which are predominantly expressed in description logics and enrich time-stamped data to temporal knowledge bases. For modeling highly complex system environments, expressive description logics are often the formalism of choice. Querying such temporal knowledge bases is a challenging task, but recently a first practical solution has been put forward. We propose a novel approach to the query answering problem based on two well-known rewriting rules from temporal logic. After a careful theoretical analysis of our algorithm, we show in a practical evaluation on several benchmarks that it outperforms state of the art, sometimes by orders of magnitude. Based on our findings, we also propose a fragment of temporal conjunctive queries which guides users towards well-performing queries
Exponential-Family Harmoniums with Neural Sufficient Statistics
No full textExponential-family harmoniums (EFHs) generalize the restricted Boltzmann machine beyond Bernoulli random variables to other exponential families. Here we show how to extend the EFH beyond standard exponential families (Poisson, Gaussian, etc.), by allowing the sufficient statistics for the hidden units to be arbitrary functions of the observed data, parameterized by deep neural networks. This rules out the standard sampling scheme, block Gibbs sampling, so we replace it with a form of Langevin dynamics within Gibbs, inspired by a recent method for training Gaussian restricted Boltzmann machines (GRBMs). With Gibbs-Langevin, the GRBM can successfully model small datasets like MNIST and CelebA-32, but struggles with CIFAR-10, and cannot scale to larger images because it lacks convolutions. In contrast, our neural-network EFHs (NN-EFHs) generate high-quality samples from CIFAR-10 and scale well to CelebA-HQ. On these datasets, the NN-EFH achieves FID scores that are 25--50% lower than a standard energy-based model with a similar neural-network architecture and the same number of parameters; and competitive with noise-conditional score networks, which utilize more complex neural networks (U-nets) and require considerably more sampling steps
List Update with Prediction
No full textList Update is a fundamental problem in online algorithms, with a well-known 2-competitive algorithm that moves every requested element to the front. Randomization can slightly improve the competitive ratio to 1.6, but not beyond 1.5. However, practical inputs are not adversarial and one hopes to do better, particularly when additional information from a machine learning oracle is available. With access to predictions, the goal is to incur only a slight overhead compared to the prediction's accuracy, avoiding significant costs in case of substantial deviation.
We propose a (1+epsilon)-smooth randomized algorithm, offering robustness of O(1/epsilon^4). This guarantees that the algorithm never exceeds a cost greater than 1+epsilon times the prediction cost, while maintaining a bound within O(1/epsilon^4) of the optimal cost for every possible sequence. In cases where no paid swaps are permitted for the prediction, we can improve robustness to O(1/epsilon^2) while retaining 1+epsilon smoothness. We complement these findings by demonstrating a lower bound of 1/epsilon on the robustness for deterministic algorithms and log(1/epsilon) for randomized ones.
Finally, the experiments we have made show that our algorithms perform better than the standard competitive algorithms for this proble
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors
No full textEvaluating deep reinforcement learning (DRL) agents against targeted behavior attacks is critical for assessing their robustness. These attacks aim to manipulate the victim into specific behaviors that align with the attacker’s objectives, often bypassing traditional reward-based defenses. Prior methods have primarily focused on reducing cumulative rewards; however, rewards are typically too generic to capture complex safety requirements effectively. As a result, focusing solely on reward reduction can lead to suboptimal attack strategies, particularly in safety-critical scenarios where more precise behavior manipulation is needed. To address these challenges, we propose RAT, a method designed for universal, targeted behavior attacks. RAT trains an intention policy that is explicitly aligned with human preferences, serving as a precise behavioral target for the adversary. Concurrently, an adversary manipulates the victim's policy to follow this target behavior. To enhance the effectiveness of these attacks, RAT dynamically adjusts the state occupancy measure within the replay buffer, allowing for more controlled and effective behavior manipulation. Our empirical results on robotic simulation tasks demonstrate that RAT outperforms existing adversarial attack algorithms in inducing specific behaviors. Additionally, RAT shows promise in improving agent robustness, leading to more resilient policies. We further validate RAT by guiding Decision Transformer agents to adopt behaviors aligned with human preferences in various MuJoCo tasks, demonstrating its effectiveness across diverse tasks
Teaching Models to Improve on Tape
No full textLarge Language Models (LLMs) often struggle when prompted to generate content under specific constraints. However, in such cases it is often easy to check whether these constraints are satisfied or violated. Recent works have shown that LLMs can benefit from such ``corrective feedback''. Here we claim that this skill of LLMs can be significantly enhanced via training. We introduce an RL framework for teaching models to use such rewards, by simulating interaction sessions, and rewarding the model according to its ability to satisfy the constraints.
We refer to our method as CORGI (Controlled Generation with RL for Guided Interaction), and evaluate it on a variety of controlled generation tasks. We find that CORGI consistently outperforms the baseline reinforcement learning method that does not incorporate conversational feedback. Furthermore, CORGI's interactive framework enables meta-learning, allowing the LLM to better generalize to guided interaction in new tasks. Our results clearly show that conversational optimization, when combined with reinforcement learning, significantly improves the effectiveness of LLMs in controlled generation contexts
The Indoor-Training Effect: Unexpected Gains from Distribution Shifts in the Transition Function
No full textIs it better to perform tennis training in a pristine indoor environment or a noisy outdoor one? To model this problem, here we investigate whether shifts in the transition probabilities between the training and testing environments in reinforcement learning problems can lead to better performance under certain conditions. We generate new Markov Decision Processes (MDPs) starting from a given MDP, by adding quantifiable, parametric noise into the transition function. We refer to this process as Noise Injection and the resulting environments as δ-environments. This process allows us to create variations of the same environment with quantitative control over noise serving as a metric of distance between environments. Conventional wisdom suggests that training and testing on the same MDP should yield the best results. In stark contrast, we observe that agents can perform better when trained on the noise-free environment and tested on the noisy δ-environments, compared to training and testing on the same δ-environments. We confirm that this finding extends beyond noise variations: it is possible to showcase the same phenomenon in ATARI game variations including varying Ghost behavior in PacMan, and Paddle behavior in Pong. We demonstrate this intriguing behavior in 60 different variations of ATARI games, including PacMan, Pong, and Breakout. We refer to this phenomenon as the Indoor-Training Effect. Code to reproduce our experiments and to implement Noise Injection
Biased Incomplete Multi-View Learning
No full textConsidering the ubiquitous phenomenon of missing views in multi-view data, incomplete multi-view learning is a crucial task in many applications. Existing methods usually follow an impute-then-predict strategy for handling this problem. However, they often assume that the view-missing patterns are uniformly random in multi-view data, which does not agree with real-world scenarios. In practice, view-missing patterns often vary across different classes. For example, in the medical field, patients with rare diseases would take more examinations than those with common diseases; in the financial field, high-risk customers tend to receive evaluations from more views than ordinary ones. Hence, we often observe that data-rich classes suffer limited views while data-poor classes suffer limited samples. Previous methods would typically fail due to such biased view-missing patterns. This motivates us to delve into a new biased incomplete multi-view learning problem. To this end, we develop a Reliable Incomplete Multi-view Learning (RIML) method. RIML is a simple yet effective learning-free imputation framework that goes beyond the conventional approaches by considering information from all classes, rather than just relying on individual views or within-class samples. Specifically, we utilize an inter-class association matrix that allows data-poor classes to refer the knowledge from data-rich classes. This enables the construction of more reliable view-specific distributions, from which we perform multiple samplings to recover missing views. Additionally, to obtain a reliable multi-view representation for downstream tasks, we develop an enhanced focal loss with a category-aware marginal term to learn a more distinguishable feature space. Experiments on five multi-view datasets demonstrate that RIML significantly outperforms existing methods in both accuracy and robustness