We study the societal tradeoffs problem, where a set of voters each submit their ideal tradeoff value between each pair of activities (e.g., "using a gallon of gasoline is as bad as creating 2 bags of landfill trash"), and these are then aggregated into the societal tradeoff vector using a rule. We introduce the family of distance-based rules and show that these can be justified as maximum likelihood estimators of the truth. Within this family, we single out the logarithmic distance-based rule as especially appealing based on a social-choice-theoretic axiomatization. We give an efficient algorithm for executing this rule as well as an approximate hill climbing algorithm, and evaluate these experimentally.

A large body of literature deals with the so-called cake cutting So how would strategic agents behave when faced with problem -- a misleadingly childish metaphor for the the cut and choose protocol? A standard way of answering challenging and important task of fairly dividing a heterogeneous this question employs the notion of Nash equilibrium: each divisible good among multiple agents (see the recent agent would use a strategy that is a best response to the other survey by Procaccia (2013) and the books by Brams agent's strategy. To set up a Nash equilibrium, suppose that and Taylor (1996) and Robertson and Webb (1998)). In particular, the first agent cuts two pieces that the second agent values there is a significant amount of AI work on cake cutting equally; the second agent selects its more preferred piece, (Procaccia 2009; Caragiannis, Lai, and Procaccia 2011; and the one less preferred by the first agent in case of a tie. Brams et al. 2012; Bei et al. 2012; Aumann, Dombb, Clearly, the second agent cannot gain from deviating, as it is and Hassidim 2013; Kurokawa, Lai, and Procaccia 2013; selecting a piece that is at least as preferred as the other. As Brânzei, Procaccia, and Zhang 2013; Brânzei and Miltersen for the first agent, if it makes its preferred piece even bigger, 2013; Chen et al. 2013; Balkanski et al. 2014; Brânzei the second agent would choose that piece, making the and Miltersen 2015; Segal-Halevi, Hassidim, and Aumann first agent worse off. Interestingly enough, in this equilibrium 2015), which is closely intertwined with emerging realworld the tables are turned; now it is the second agent who applications of fair division more broadly (Goldman is getting exactly half of its value for the whole cake, while and Procaccia 2014; Kurokawa, Procaccia, and Shah 2015).

We study efficient algorithms for a natural learning problem in markets. There is one seller with m divisible goods and n buyers with unknown individual utility functions and budgets of money. The seller can repeatedly announce prices and observe aggregate demand bundles requested by the buyers. The goal of the seller is to learn the utility functions and budgets of the buyers. Our scenario falls into the classic domain of ''revealed preference'' analysis. Problems with revealed preference have recently started to attract increased interest in computer science due to their fundamental nature in understanding customer behavior in electronic markets. The goal of revealed preference analysis is to observe rational agent behavior, to explain it using a suitable model for the utility functions, and to predict future agent behavior. Our results are the first polynomial-time algorithms to learn utility and budget parameters via revealed preference queries in classic Fisher markets with multiple buyers. Our analysis concentrates on linear, CES, and Leontief markets, which are the most prominent classes studied in the literature. Some of our results extend to general Arrow-Debreu exchange markets.

We study an important crowdsourcing setting where agents evaluate one another and, based on these evaluations, a subset of agents are selected. This setting is ubiquitous when peer review is used for distributing awards in a team, allocating funding to scientists, and selecting publications for conferences. The fundamental challenge when applying crowdsourcing in these settings is that agents may misreport their reviews of others to increase their chances of being selected. We propose a new strategyproof (impartial) mechanism called Dollar Partition that satisfies desirable axiomatic properties. We then show, using a detailed experiment with parameter values derived from target real world domains, that our mechanism performs better on average, and in the worst case, than other strategyproof mechanisms in the literature.

We study the Maximum Weighted Matching problem in a partial information setting where the agents' utilities for being matched to other agents are hidden and the mechanism only has access to ordinal preference information. Our model is motivated by the fact that in many settings, agents cannot express the numerical values of their utility for different outcomes, but are still able to rank the outcomes in their order of preference. Specifically, we study problems where the ground truth exists in the form of a weighted graph, and look to design algorithms that approximate the true optimum matching using only the preference orderings for each agent (induced by the hidden weights) as input. If no restrictions are placed on the weights, then one cannot hope to do better than the simple greedy algorithm, which yields a half optimal matching. Perhaps surprisingly, we show that by imposing a little structure on the weights, we can improve upon the trivial algorithm significantly: we design a 1.6-approximation algorithm for instances where the hidden weights obey the metric inequality. Our algorithm is obtained using a simple but powerful framework that allows us to combine greedy and random techniques in unconventional ways. These results are the first non-trivial ordinal approximation algorithms for such problems, and indicate that we can design robust matchings even when we are agnostic to the precise agent utilities.

This paper studies the impact of Time-Of-Use (TOU) tariffs in a competitive electricity market place. Specifically, it focuses on the question of how should an autonomous broker agent optimize TOU tariffs in a competitive retail market, and what is the impact of such tariffs on the economy. We formalize the problem of TOU tariff optimization and propose an algorithm for approximating its solution. We extensively experiment with our algorithm in a large-scale, detailed electricity retail markets simulation of the Power Trading Agent Competition (Power TAC) and: 1) find that our algorithm results in 15% peak-demand reduction, 2) find that its peak-flattening results in greater profit and/or profit-share for the broker and allows it to win against the 1st and 2nd place brokers from the Power TAC 2014 finals, and 3) analyze several economic implications of using TOU tariffs in competitive retail markets.

An important use of machine learning is to learn what people value. What posts or photos should a user be shown? Which jobs or activities would a person find rewarding? In each case, observations of people's past choices can inform our inferences about their likes and preferences. If we assume that choices are approximately optimal according to some utility function, we can treat preference inference as Bayesian inverse planning. That is, given a prior on utility functions and some observed choices, we invert an optimal decision-making process to infer a posterior distribution on utility functions. However, people often deviate from approximate optimality. They have false beliefs, their planning is sub-optimal, and their choices may be temporally inconsistent due to hyperbolic discounting and other biases. We demonstrate how to incorporate these deviations into algorithms for preference inference by constructing generative models of planning for agents who are subject to false beliefs and time inconsistency. We explore the inferences these models make about preferences, beliefs, and biases. We present a behavioral experiment in which human subjects perform preference inference given the same observations of choices as our model. Results show that human subjects (like our model) explain choices in terms of systematic deviations from optimal behavior and suggest that they take such deviations into account when inferring preferences.

Accenture announced the global rollout of an intelligent automation platform, Accenture myWizard, that enables services consisting of systems integration and application development and management. The platform combines Accenture's industry and technology assets and business knowledge with intelligent automation, including artificial intelligence at its core. Accenture myWizard supports productivity improvement for clients by employing a team of virtual agents, powered by AI, to analyze data, identify patterns and guide human workers to make better-informed decisions. This can support business performance which can include significant improvement in application quality, cost reduction and speed to market. The new platform brings together several Accenture industry aspects, including intelligent and analytics tools and methods, as well as tools from across Accenture's alliance partner ecosystem.

I am a transitional research fellow in the Digital Creativity Hub at the University of York working on Artificial Intelligence (AI), data mining and machine learning for digital games and interactive media. I am also a member of the Artificial Intelligence and Games groups, in the Department of Computer Science. My research is focussed on using games to push boundaries in the capabilities of modern AI and making state of the art methods accessible to the industry to encourage a new generation of intelligent games.

The 2016 winners were as follows: Tom Dietterich, AAAI President, for AAAI 2017 Awards, please Manuela Veloso, AAAI Past President contact Carol Hamilton at hamilton@aaai.org.