Deciding the outcome of an election when voters have provided only partial orderings over their preferences requires voting rules that accommodate missing data. While existing techniques, including considerable recent work, address missingness through circumvention, we propose the novel application of conventional machine learning techniques to predict the missing components of ballots via latent patterns in the information that voters are able to provide. We show that suitable predictive features can be extracted from the data, and demonstrate the high performance of our new framework on the ballots from many real world elections, including comparisons with existing techniques for voting with partial orderings. Our technique offers a new and interesting conceptualization of the problem, with stronger connections to machine learning than conventional social choice techniques.

In many situations, it is desirable to optimize a sequence of decisions by maximizing a primary objective while respecting some constraints with respect to secondary objectives. Such problems can be naturally modeled as constrained partially observable Markov decision processes (CPOMDPs) when the environment is partially observable. In this work, we describe a technique based on approximate linear programming to optimize policies in CPOMDPs. The optimization is performed offline and produces a finite state controller with desirable performance guarantees. The approach outperforms a constrained version of point-based value iteration on a suite of benchmark problems.

We propose SoF (Soft-cluster matrix Factorization), a probabilistic clustering algorithm which softly assigns each data point into clusters. Unlike model-based clustering algorithms, SoF does not make assumptions about the data density distribution. Instead, we take an axiomatic approach to define 4 properties that the probability of co-clustered pairs of points should satisfy. Based on the properties, SoF utilizes a distance measure between pairs of points to induce the conditional co-cluster probabilities. The objective function in our framework establishes an important connection between probabilistic clustering and constrained symmetric Nonnegative Matrix Factorization (NMF), hence providing a theoretical interpretation for NMF-based clustering algorithms. To optimize the objective, we derive a sequential minimization algorithm using a penalty method. Experimental results on both synthetic and real-world datasets show that SoF significantly outperforms previous NMF-based algorithms and that it is able to detect non-convex patterns as well as cluster boundaries.

We consider the problem of repeatedly matching a set of alternatives to a set of agents in the absence of monetary transfer. We propose a generic framework for evaluating sequential matching mechanisms with dynamic preferences, and show that unlike single-shot settings, the random serial dictatorship mechanism is manipulable.

We consider the problem of repeatedly matching a set of alternatives to a set of agents with dynamic ordinal preferences. Despite a recent focus on designing one-shot matching mechanisms in the absence of monetary transfers, little study has been done on strategic behavior of agents in sequential assignment problems. We formulate a generic dynamic matching problem via a sequential stochastic matching process. We design a mechanism based on random serial dictatorship (RSD) that, given any history of preferences and matching decisions, guarantees global stochastic strategyproofness while satisfying desirable local properties. We further investigate the notion of envyfreeness in such sequential settings.

Within the field of Artificial Intelligence (AI) research, Vote (STV) select winners from sets of ballot. For example, the subfield of Computational Social Choice considers under plurality candidate X has won this election by virtue the application of AI techniques to problems in Social of having being at the top of the largest number of ballots. Under STV Y would win instead. Starting in the early 1990's, computer scientists began to In this work, we model voters as having partial orderings take an interest in social choice. Initial work was concerned over the candidates for their preferences, instead of linear with circumventing the impossibility results implied orderings.

In this paper we revisit the problem of generating query plans using AI automated planning with a view to leveraging significant recent advances in state-of-the-art planning techniques. Our efforts focus on the specific problem of cost-based join-order optimization for conjunctive relational queries, a critical component of production-quality query optimizers. We characterize the general query-planning problem as a delete-free planning problem, and query plan optimization as a context-sensitive cost-optimal planning problem. We propose algorithms that generate high-quality query plans, guaranteeing optimality under certain conditions. Our approach is general, supporting the use of a broad suite of domain-independent and domain-specific optimization criteria. Experimental results demonstrate the effectiveness of AI planning techniques for query plan generation and optimization.

As a promising alternative to using standard (often intractable) planning techniques with Bellman equations, we propose an interesting method of optimizing POMDP controllers by probabilistic inference in a novel equivalent single-DBN generative model. Our inference approach to POMDP planning allows for (1) for application of various techniques for probabilistic inference in single graphical models, and (2) for exploiting the factored structure in a controller architecture to take advantage of natural structural constrains of planning problems and represent them compactly. Our contributions can be summarized as follows: (1) we designed a novel single-DBN generative model that ensures that the task of probabilistic inference is equivalent to the original problem of optimizing POMDP controllers, and (2) we developed several inference approaches to approximate the value of the policy when exact inference methods are not tractable to solve large-size problems with complex graphical models. The proposed approaches to policy optimization by probabilistic inference are evaluated on several POMDP benchmark problems and the performance of the implemented approximation algorithms is compared.

Wildland fires (or wildfires) occur on all continents except for Antarctica. These fires threaten communities, change ecosystems, destroy vast quantities of natural resources and the cost estimates of the damage done annually is in the billions of dollars. Controlling wildland fires is resource-intensive and there are numerous examples where the resource demand has outstripped resource availability. Trends in changing climates, fire occurrence and the expansion of the wildland-urban interface all point to increased resource shortages in the future. One approach for coping with these shortages has been the sharing of resources across different wildland-fire agencies. This introduces new issues as agencies have to balance their own needs and risk-management with their desire to help fellow agencies in need. Using ideas from the field of multiagent systems, we conduct the first analysis of strategic issues arising in resource-sharing for wildland-fire control. We also argue that the wildland-fire domain has numerous features that make it attractive to researchers in artificial intelligence and computational sustainability.

In this paper we discuss the gap that exists between the caregivers of older adults attempting to age-in-place and sophisticated ”smart-home” systems that can sense the environment and provide assistance when needed. We argue that smart-home systems need to be customizable by end-users, and we present a general-purpose model for cognitive assistive technology that can be adapted to suit many different tasks, users and environments. Al- though we can provide mechanisms for engineers and designers to build and adapt smart-home systems based on this general-purpose model, these mechanisms are not easily understood by or sufficiently user-friendly for actual end users such as older adults and their care- givers. Our goal is therefore to study how to bridge the gap between the end-users and this technology. In this paper, we discuss our work on this problem from both sides: developing technology that is customizable and general-purpose, and studying user’s abilities and needs when it comes to building smart-home systems to help with activities of daily living. We show how a large gap still exists, and propose ideas for how to bridge the gap.