# John N. HookerCarnegie Mellon University | CMU · Tepper School of Business

John N. Hooker

PhD

## About

346

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Introduction

## Publications

Publications (346)

Statistical parity metrics have been widely studied and endorsed in the AI community as a means of achieving fairness, but they suffer from at least two weaknesses. They disregard the actual welfare consequences of decisions and may therefore fail to achieve the kind of fairness that is desired for disadvantaged groups. In addition, they are often...

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-o...

We revisit the question initially raised by Yuji Ijiri about the notion of fairness in accounting. We argue that the fairness question was important then and remains relevant today. First we situate Ijiri’s question in relevant debates in the history of accounting thoughts and in contemporary debates. Then we develop a framework of fair flow of inf...

Ride-hailing services have expanded the role of shared mobility in passenger transportation systems, creating new markets and creative planning solutions for major urban centers. In this paper, we consider their use for the first-mile or last-mile passenger transportation in coordination with a mass transit service to provide a seamless multimodal...

A small instance exemplifying the structure used in the paper is provided in the data folder. The collection of instances used in the paper and their description can be found in the website for the MERL Last Mile Problem Set.

This chapter presents several variations and special cases of logic-based Benders decomposition (LBBD). The most important variation is branch and check, which solves the master problem only once. Other variations include enumerative methods, multilevel decomposition, and dynamic variable partitioning. Special cases include stochastic and robust LB...

This chapter, which comprises nearly half the book, is a compendium of 147 published applications of logic-based Benders decomposition (LBBD), classified by category and subcategory. It describes how 226 articles from the technical literature adapt the LBBD framework to these problems, with a focus on the decomposition into master problem and subpr...

This chapter develops the elementary theory of logic-based Benders decomposition (LBBD), beginning with the essential concept of inference duality. It formally states the LBBD algorithm and proves finite convergence when certain variables have finite domains, a condition normally satisfied in practice. It shows how classical Benders decomposition i...

Effective logic-based cuts are essential to the success of logic-based Benders decomposition, and this chapter shows how they can be designed to exploit problem structure. The most popular cuts used in practice are strengthened nogood cuts and analytical cuts. Nogood cuts are based on optimal values returned from the Benders subproblem, and several...

Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that i...

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-o...

We introduce stochastic decision diagrams (SDDs) as a generalization of deterministic decision diagrams, which in recent years have been used to solve a variety of discrete optimization and constraint satisfaction problems. SDDs allow one to extend the relaxation techniques of deterministic diagrams to stochastic dynamic programming problems in whi...

We apply logic-based Benders decomposition (LBBD) to two-stage stochastic planning and scheduling problems in which the second stage is a scheduling task. We solve the master problem with mixed integer/linear programming and the subproblem with constraint programming. As Benders cuts, we use simple no-good cuts as well as analytic logic-based cuts...

The primary role of cutting planes is to separate fractional solutions of the linear programming relaxation, which results in tighter bounds for pruning the search tree and reducing its size. Bounding, however, has an indirect impact on the size of the search tree. Cutting planes can also reduce backtracking by excluding inconsistent partial assign...

Geographical considerations such as contiguity and compactness are necessary elements of political districting in practice. Yet an analysis of the problem without such constraints yields mathematical insights that can inform real-world model construction. In particular, it clarifies the sharp contrast between proportionality and competitiveness and...

In this chapter, we address the problem of humanizing business when we must interact with intelligent robots and other AI systems, rather than real people, on a daily basis. There is a strong tendency to anthropomorphize pets and other animals that carries over to smart machines, leading us to replace human relationships with something less sophist...

A trade-off between fairness and efficiency is an important element of many practical decisions. We propose a principled and practical method for balancing these two criteria in an optimization model. Following an assessment of existing schemes, we define a set of social welfare functions (SWFs) that combine Rawlsian leximax fairness and utilitaria...

Given a set of agents, the multi-agent pathfinding problem consists in determining, for each agent, a path from its start location to its assigned goal while avoiding collisions with other agents. Recent work has studied variants of the problem in which agents are assigned a sequence of goals (tasks) that become available over time, such as the onl...

Conversational Artificial Intelligence (AI) used in industry settings can be trained to closely mimic human behaviors, including lying and deception. However, lying is often a necessary part of negotiation. To address this, we develop a normative framework for when it is ethical or unethical for a conversational AI to lie to humans, based on whethe...

An important step in the development of value alignment (VA) systems in artificial intelligence (AI) is understanding how VA can reflect valid ethical principles. We propose that designers of VA systems incorporate ethics by utilizing a hybrid approach in which both ethical reasoning and empirical observation play a role. This, we argue, avoids com...

We propose optimization as a general paradigm for formalizing fairness in AI-based decision models. We argue that optimization models allow formulation of a wide range of fairness criteria as social welfare functions, while enabling AI to take advantage of highly advanced solution technology. We show how optimization models can assist fairness-orie...

We apply logic-based Benders decomposition (LBBD) to two-stage stochastic planning and scheduling problems in which the second-stage is a scheduling task. We solve the master problem with mixed integer/linear programming and the subproblem with constraint programming. As Benders cuts, we use simple nogood cuts as well as analytical logic-based cuts...

An important step in the development of value alignment (VA) systems in AI is understanding how VA can reflect valid ethical principles. We propose that designers of VA systems incorporate ethics by utilizing a hybrid approach in which both ethical reasoning and empirical observation play a role. This, we argue, avoids committing the "naturalistic...

Optimization models generally aim for efficiency by maximizing total benefit or minimizing cost. Yet a trade-off between fairness and efficiency is an important element of many practical decisions. We propose a principled and practical method for balancing these two criteria in an optimization model. Following a critical assessment of existing sche...

There is widespread concern that as machines move toward greater autonomy, they may become a law unto themselves and turn against us. Yet the threat lies more in how we conceive of an autonomous machine rather than the machine itself. We tend to see an autonomous agent as one that sets its own agenda, free from external constraints, including ethic...

Society 5.0 will rest fundamentally on advanced algorithms, but will people trust them? This brief essay examines some factors that may influence future acceptance or rejection of a cybernetically-integrated society. These include algorithmic honesty, competence, transparency, and flexibility, as well as our willingness to relate appropriately to n...

Shared mobility is revolutionizing urban transportation and sparked interest on optimizing the joint schedule of passengers in public transit and last-mile services. Scheduling algorithms must anticipate future requests and provision flexibility in order to be adopted in practice. In this work, we consider a two-stage stochastic programming formula...

This paper is an informal survey of some of the deep connections between logic and optimization. It covers George Boole's probability logic, decision diagrams, logic and cutting planes, first order predicate logic, default and nonmonotonic logics, logic and duality, and finite-domain constraint programming. There is particular emphasis on practical...

Logic-based Benders decomposition (LBBD) is a substantial generalization of classical Benders decomposition that, in principle, allows the subproblem to be any optimization problem rather than specifically a linear or nonlinear programming problem. It is amenable to a wide variety large-scale problems that decouple or otherwise simplify when certai...

We introduce a general method for relaxing decision diagrams that allows one to bound job sequencing problems by solving a Lagrangian dual problem on a relaxed diagram. We also provide guidelines for identifying problems for which this approach can result in useful bounds. These same guidelines can be applied to bounding deterministic dynamic progr...

Logic-based Benders decomposition (LBBD) is a substantial generalization of classical Benders decomposition that, in principle, allows the subproblem to be any optimization problem rather than specifically a linear or nonlinear programming problem. It is amenable to a wide variety of large-scale problems that decouple or otherwise simplify when cer...

We present a general method for obtaining strong bounds for discrete optimization problems that is based on a concept of branching duality. It can be applied when no useful integer programming model is available, and we illustrate this with the minimum bandwidth problem. The method strengthens a known bound for a given problem by formulating a dual...

We introduce a general method for relaxing decision diagrams that allows one to bound job sequencing problems by solving a Lagrangian dual problem on a relaxed diagram. We also provide guidelines for identifying problems for which this approach can result in useful bounds. These same guidelines can be applied to bounding deterministic dynamic progr...

We present a general method for obtaining strong bounds for discrete optimization problems that is based on a concept of branching duality. It can be applied when no useful integer programming model is available, and we illustrate this with the minimum bandwidth problem. The method strengthens a known bound for a given problem by formulating a dual...

An important step in the development of value alignment (VA) systems in AI is understanding how values can interrelate with facts. Designers of future VA systems will need to utilize a hybrid approach in which ethical reasoning and empirical observation interrelate successfully in machine behavior. In this article we identify two problems about thi...

It is often useful in practice to explore near-optimal solutions of an integer programming problem. We show how all solutions within a given tolerance of the optimal value can be efficiently and compactly represented in a weighted decision diagram. The structure of the diagram facilitates rapid processing of a wide range of queries about the near-o...

We propose an exact optimization method for home healthcare delivery that relies on logic-based Benders decomposition (LBBD). The objective is to match patients with healthcare aides and schedule multiple home visits over a given time horizon, to maximize the number of patients served while taking into account patient requirements, travel time, and...

While many see the prospect of autonomous machines as threatening, autonomy may be exactly what we want in a superintelligent machine. There is a sense of autonomy, deeply rooted in the ethical literature, in which an autonomous machine is necessarily an ethical one. Development of the theory underlying this idea not only reveals the advantages of...

The hamiltonian circuit polytope is the convex hull of feasible solutions for the circuit constraint, which provides a succinct formulation of the traveling salesman and other sequencing problems. We study the polytope by establishing its dimension, developing tools for the identification of facets, and using these tools to derive several families...

Concepts of consistency have long played a key role in constraint programming but never developed in integer programming (IP). Consistency nonetheless plays a role in IP as well. For example, cutting planes can reduce backtracking by achieving various forms of consistency as well as by tightening the linear programming (LP) relaxation. We introduce...

Value alignment" (VA) is considered as one of the top priorities in AI research. Much of the existing research focuses on the "A" part and not the "V" part of "value alignment." This paper corrects that neglect by emphasizing the "value" side of VA and analyzes VA from the vantage point of requirements in value theory, in particular, of avoiding th...

"Value alignment" (VA) is considered as one of the top priorities in AI research. Much of the existing research focuses on the "A" part and not the "V" part of "value alignment." This paper corrects that neglect by emphasizing the "value" side of VA and analyzes VA from the vantage point of requirements in value theory, in particular, of avoiding t...

Presentation of the work related to seamless multimodal transportation scheduling - https://arxiv.org/abs/1807.09676

Businesses are rapidly automating workplaces with new technologies (e.g., driverless cargo trucks, artificially intelligent mortgage approvals, machine-learning based paralegals, algorithmic managers). Such technological advancement raises a host of questions for business and society. As Thomas Donaldson recently remarked, “It’s instance of a probl...

Ride-hailing services have expanded the role of shared mobility in passenger transportation systems, creating new markets and creative planning solutions for major urban centers. In this paper, we consider their use for last-mile passenger transportation in coordination with a mass transit service to provide a seamless multimodal transportation exp...

Ride-hailing services have expanded the role of shared mobility in passenger transportation systems, creating new markets and creative planning solutions for major urban centers. In this paper, we consider their use for last-mile passenger transportation in coordination with a mass transit service to provide a seamless multimodal transportation exp...

Last-mile transportation (LMT) refers to any service that moves passengers from a hub of mass transportation (MT), such as air, boat, bus, or train, to destinations, such as a home or an office. In this paper, we introduce the problem of scheduling passengers jointly on MT and LMT services, with passengers sharing a car, van, or autonomous pod of l...

Last-mile transportation (LMT) refers to any service that moves passengers from a hub of mass transportation (MT), such as air, boat, bus, or train, to destinations, such as a home or an office. In this paper, we introduce the problem of scheduling passengers jointly on MT and LMT services, with passengers sharing a car, van, or autonomous pod of l...

We present an overview of the integration of constraint programming (CP) and operations research (OR) to solve combinatorial optimization problems. We interpret CP and OR as relying on a common primal-dual solution approach that provides the basis for integration using four main strategies. The first strategy tightly interweaves propagation from CP...

This book constitutes the proceedings of the 24th International Conference on Principles and Practice of Constraint Programming, CP 2018, held in Lille, France, in August 2018.
The 41 full and 9 short papers presented in this volume were carefully reviewed and selected from 114 submissions. They deal with all aspects of computing with constraints i...

We propose a deontological approach to machine ethics that avoids some weaknesses of an intuition-based system, such as that of Anderson and Anderson. In particular, it has no need to deal with conflicting intuitions, and it yields a more satisfactory account of when autonomy should be respected. We begin with a "dual standpoint" theory of action t...

In recent research, decision diagrams have proved useful for the solution of discrete optimization problems. Their success relies on the use of relaxed decision diagrams to obtain bounds on the optimal value, either through a node merger or a node splitting mechanism. We investigate the potential of node merger to provide bounds for dynamic program...

We analyze results of a search for alternative musical scales that share the main advantages of classical scales: pitch frequencies that bear simple ratios to each other, and multiple keys based on an underlying chromatic scale with tempered tuning. The search is based on combinatorics and a constraint programming model that assigns frequency ratio...

Logic-based Benders decomposition (LBBD) has improved the state of the art for solving a variety of planning and scheduling problems, in part by combining the complementary strengths of constraint programming and mixed integer programming (MIP). We undertake a computational analysis of specific factors that contribute to the success of LBBD, to pro...

While many see the prospect of autonomous machines as threatening, autonomy may be exactly what we want in a superintelligent machine. There is a sense of autonomy, deeply rooted in the ethical literature, in which an autonomous machine is necessarily an ethical one. Development of the theory underlying this idea not only reveals the advantages of...

This chapter presents a general-purpose methodology for obtaining a set of feasible solutions to a discrete optimization problems using restricted decision diagrams. A restricted diagram can be perceived as a counterpart of the concept of relaxed diagrams introduced in previous chapters, and represents an under approximation of the feasible set, th...

This chapter provides a brief review of the literature on decision diagrams, primarily as it relates to their use in optimization and constraint programming. It begins with an early history of decision diagrams and their relation to switching circuits. It then surveys some of the key articles that brought decision diagrams into optimization and con...

This chapter proposes an alternative branch-and-bound method in which decision diagrams take over the functions of the traditional relaxations and heuristics used in general-purpose optimization techniques. In particular, we show an enumeration scheme that branches on the nodes of a relaxed decision diagram, as opposed to variable-value assignments...

One of the most important parameters that determines the size of a decision diagram is the variable ordering. In this chapter we formally study the impact of variable ordering on the size of exact decision diagrams for the maximum independent set problem. We provide worst-case bounds on the size of the exact decision diagram for particular classes...

This is the first of three chapters that apply decision diagrams in the context of constraint programming. This chapter starts by providing a background of the solving process of constraint programming, focusing on consistency notions and constraint propagation. We then extend this methodology to MDD-consistency and MDD-based constraint propagation...

In this chapter we present a detailed study of MDD propagation for the sequence constraint. This constraint can be applied to combinatorial problems such as employee rostering and car manufacturing. It will serve to illustrate the main challenges when studying MDD propagation for a new constraint type: Tractability, design of the propagation algori...

This chapter focuses on the type of recursive modeling that is required for solution by decision diagrams. It presents a formal development that highlights how solution by decision diagrams differs from traditional enumeration of the state space. It illustrates the versatility of recursive modeling with examples: single facility scheduling, schedul...

Bounds on the optimal value are often indispensable for the practical solution of discrete optimization problems, as for example in branch-and-bound procedures. This chapter explores an alternative strategy of obtaining bounds through relaxed decision diagrams, which overapproximate both the feasible set and the objective function of the problem. W...

In this chapter we provide an in-depth study of representing and handling single-machine scheduling and sequencing problems with decision diagrams. We provide exact and relaxed MDD representations, together with MDD filtering algorithms for various side constraints, including time windows, precedence constraints, and sequence-dependent setup times....

In this chapter we introduce a modeling framework based on dynamic programming to compile exact decision diagrams. We describe how dynamic programming models can be used in a top-down compilation method to construct exact decision diagrams. We also present an alternative compilation method based on constraint separation. We illustrate our framework...

We search for alternative musical scales that share the main advantages of classical scales: pitch frequencies that bear simple ratios to each other, and multiple keys based on an underlying chromatic scale with tempered tuning. We conduct the search by formulating a constraint satisfaction problem that is well suited for solution by constraint pro...

We propose an exact optimization method for home hospice care staffing and scheduling, using logic-based Benders decomposition (LBBD). The objective is to match hospice care aides with patients and schedule visits to patient homes, so as to maximize the number of patients serviced by available staff, while meeting requirements of the patient plan o...

We study project scheduling at a large IT services delivery center in which there are unpredictable delays. We apply robust optimization to minimize tardiness while informing the customer of a reasonable worst-case completion time, based on empirically determined uncertainty sets. We introduce a new solution method based on logic-based Benders deco...

We propose a general branch-and-bound algorithm for discrete optimization in which binary decision diagrams (BDDs) play the role of the traditional linear programming relaxation. In particular, relaxed BDD representations of the problem provide bounds and guidance for branching, and restricted BDDs supply a primal heuristic. Each problem is given a...

This book introduces a novel approach to discrete optimization, providing both theoretical insights and algorithmic developments that lead to improvements over state-of-the-art technology. The authors present chapters on the use of decision diagrams for combinatorial optimization and constraint programming, with attention to general-purpose solutio...