Dr. Kristen B Venable

Forest disturbance due to natural events, such as wildfires, represents an increasing global challenge that demands advanced analytical methods for effective detection and mitigation. To this end, integrating satellite imagery with deep learning (DL) has emerged as a powerful approach for forest wildfire detection; however, its practical use remains limited by the scarcity of large, well-labeled satellite imagery datasets. In this study, we address this issue by presenting the California Wildfire GeoImaging Dataset (CWGID), a high-resolution bi-temporal collection of over 100,000 labeled RGB "before-" and and "-after" Sentinel-2 wildfire satellite image pairs. We build and label the dataset programmatically, significantly reducing the time and manual effort usually required to create labeled datasets suitable for DL applications. Our methods include data acquisition from authoritative sources, systematic preprocessing, and an initial analysis using three pre-trained Convolutional Neural Network (CNN) architectures for two classification tasks consisting, respectively, in labeling unitemporal and bitemporal inputs as damaged or not damaged by fire. Our results show that using bi-temporal imagery as input during model training and testing can result in improved model performance, with the Early Fusion (EF) EfficientNet-B0 model achieving the highest wildfire detection accuracy of over 92%. These findings suggest that the CWGID and the streamlined programmatic methodology used to build it may help address the scarcity of labeled data for DL-based forest wildfire detection, while providing a scalable resource that could support other DL applications in environmental monitoring.

Inspired by the ”thinking fast and slow” cognitive theory of human decision making, we propose a multi-agent cognitive architecture (SOFAI) that is based on ”fast”/”slow” solvers and a metacognitive module. We then present experimental results on the behavior of an instance of this architecture for AI systems that make decisions about navigating in a constrained environment. We show that combining the two decision modalities through a separate metacognitive function allows for higher decision quality with less resource consumption compared to employing only one of the two modalities. Analyzing how the system achieves this, we also provide evidence for the emergence of several human-like behaviors, including skill learning, adaptability, and cognitive control.

The stable marriage (SM) problem has a wide variety of practical applications, ranging from matching resident doctors to hospitals, to matching students to schools or, more generally, to any two-sided market. In the classical formulation, n men and n women express their preferences (via a strict total order) over the members of the other sex. Solving an SM problem means finding a stable marriage where stability is an envy-free notion: no man and woman who are not married to each other would both prefer each other to their partners or to being single. We consider both the classical stable marriage problem and one of its useful variations (denoted SMTI (Stable Marriage with Ties and Incomplete lists)) where the men and women express their preferences in the form of an incomplete preference list with ties over a subset of the members of the other sex. Matchings are permitted only with people who appear in these preference lists, and we try to find a stable matching that marries as many people as possible. Whilst the SM problem is polynomial to solve, the SMTI problem is NP-hard. We propose to tackle both problems via a local search approach, which exploits properties of the problems to reduce the size of the neighborhood and to make local moves efficiently. We empirically evaluate our algorithm for SM problems by measuring its runtime behavior and its ability to sample the lattice of all possible stable marriages. We evaluate our algorithm for SMTI problems in terms of both its runtime behavior and its ability to find a maximum cardinality stable marriage. Experimental results suggest that for SM problems, the number of steps of our algorithm grows only as O(n log (n)), and that it samples very well the set of all stable marriages. It is thus a fair and efficient approach to generate stable marriages. Furthermore, our approach for SMTI problems is able to solve large problems, quickly returning stable matchings of large and often optimal size, despite the NP-hardness of this problem.

We investigate the computational complexity of finding optimal bribery schemes in voting domains where the candidate set is the Cartesian product of a set of variables and voters use CP-nets, an expressive and compact way to represent preferences. To do this, we generalize the traditional bribery problem to take into account several issues over which agents vote, and their inter-dependencies. We consider five voting rules, three kinds of bribery actions, and five cost schemes. For most of the combinations of these parameters, we find that bribery in this setting is computationally easy.

In multiagent settings where agents have different preferences, preference aggregation can be an important issue. Voting is a general method to aggregate preferences. We consider the use of voting tree rules to aggregate agents' preferences. In a voting tree, decisions are taken by performing a sequence of pairwise comparisons in a binary tree where each comparison is a majority vote among the agents. Incompleteness in the agents' preferences is common in many real-life settings due to privacy issues or an ongoing elicitation process. We study how to determine the winners when preferences may be incomplete, not only for voting tree rules (where the tree is assumed to be fixed), but also for the Schwartz rule (in which the winners are the candidates winning for at least one voting tree). In addition, we study how to determine the winners when only balanced trees are allowed. In each setting, we address the complexity of computing necessary (respectively, possible) winners, which are those candidates winning for all completions (respectively, at least one completion) of the incomplete profile. We show that many such winner determination problems are computationally intractable when the votes are weighted. However, in some cases, the exact complexity remains unknown. Since it is generally computationally difficult to find the exact set of winners for voting trees and the Schwartz rule, we propose several heuristics that find in polynomial time a superset of the possible winners and a subset of the necessary winners which are based on the completions of the (incomplete) majority graph built from the incomplete profiles.

We consider how to combine the preferences of multiple agents despite the presence of incompleteness and incomparability in their preference relations over possible candidates. The set of preference relations of an agent may be incomplete because, for example, there is an ongoing preference elicitation process. There may also be incomparability as this is useful, for example, in multi-criteria scenarios. We focus here on the problem of computing the
possible and necessary winners, that is, those candidates which can be, or always are, the most preferred for the agents. Possible and necessary winners are useful in many scenarios including preference elicitation. First, we show that testing possible and necessary winners is in general a computationally intractable problem for STV with unweighted votes and the cup rule with weighted votes, as is providing a good approximation of such sets. Then, we identify general properties of the preference aggregation function, such as independence to irrelevant alternatives, which are sufficient for such sets to be computed in polynomial time. Finally, we show how possible and necessary winners can be used to focus preference elicitation. We show that it is computationally intractable for the cup rule with weighted votes in the worst-case to decide when to terminate elicitation. However, we identify a property of the preference aggregation function that allows us to decide when to terminate elicitation in polynomial time, by focusing on possible and necessary winners.

Real-life problems present several kinds of preferences. We focus on problems with both positive and negative preferences, which we call bipolar preference problems. Although seemingly specular notions, these two kinds of preferences should be dealt with differently to obtain the desired natural behaviour. We technically address this by generalising the soft constraint formalism, which is able to model problems with one kind of preference. We show that soft constraints model only negative preferences, and we add to them a new mathematical structure which allows to handle positive preferences as well. We also address the issue of the compensation between positive and negative preferences, studying the properties of this operation. Finally, we extend the notion of arc consistency to bipolar problems, and we show how branch and bound (with or without constraint propagation) can be easily adapted to solve such problems.

We consider soft constraint problems where some of the preferences may be unspecified. This models, for example, settings where agents are distributed and have privacy issues, or where there is an ongoing preference elicitation process. In this context, we study how to find an optimal solution without having to wait for all the preferences. In particular, we define algorithms, that interleave search and preference elicitation, to find a solution which is necessarily optimal, that is, optimal no matter what the missing data will be, with the aim to ask the user to reveal as few preferences as possible. We define a combined solving and preference elicitation scheme with a large number of different instantiations, each corresponding to a concrete algorithm, which we compare experimentally. We compute both the number of elicited preferences and the user effort, which may be larger, as it contains all the preference values the user has to compute to be able to respond to the elicitation requests. While the number of elicited preferences is important when the concern is to communicate as little information as possible, the user effort measures also the hidden work the user has to do to be able to communicate the elicited preferences. Our experimental results on classical, fuzzy, weighted and temporal incomplete CSPs show that some of our algorithms are very good at finding a necessarily optimal solution while asking the user for only a very small fraction of the missing preferences. The user effort is also very small for the best algorithms. (C) 2009 Elsevier B.V. All rights reserved.

Preferences are not always expressible via complete inear orders: sometimes it is more natural to allow for the presence of incomparable outcomes. This may hold both in the agents preference ordering and in the social order. In this article, we consider this scenario and study what properties it may have. In particular, we show that, despite the added expressivity and ability to resolve conflicts provided by incomparability, classical impossibility results (such as Arrows theorem, MullerSatterthwaites theorem and GibbardSatterthwaites theorem) still hold. We also prove some possibility results, generalizing Sens theorem for majority voting. To prove these results, we define new notions of unanimity, monotonicity, dictator, triple-wise value-restriction and strategy-proofness, which are suitable and natural generalizations of the classical ones for complete orders.

We review constraint-based approaches to handle preferences. We start by defining the main notions of constraint programming and then give various concepts of soft constraints and show how they can be used to model quantitative preferences. We then consider how soft constraints can be adopted to handle other forms of preferences, such as bipolar, qualitative, and temporal preferences. Finally, we describe how AI techniques such as abstraction, explanation generation, machine learning, and preference elicitation can be useful in modeling and solving soft constraints.