With proper management, Autonomous Mobility-on-Demand (AMoD) systems have great potential to satisfy the transport demands of urban populations by providing safe, convenient, and affordable ridesharing services. Meanwhile, such systems can substantially decrease private car ownership and use, and thus significantly reduce traffic congestion, energy consumption, and carbon emissions. To achieve this objective, an AMoD system requires private information about the demand from passengers. However, due to self-interestedness, passengers are unlikely to cooperate with the service providers in this regard. Therefore, an online mechanism is desirable if it incentivizes passengers to truthfully report their actual demand. For the purpose of promoting ridesharing, we hereby introduce a posted-price, integrated online ridesharing mechanism (IORS) that satisfies desirable properties such as ex-post incentive compatibility, individual rationality, and budget-balance. Numerical results indicate the competitiveness of IORS compared with two benchmarks, namely the optimal assignment and an offline, auction-based mechanism.

The candidate will join the AI lab (ai.vub.ac.be) of the Department of Computer Science. The candidate is expected to contribute to the research and teaching of the AI team. The lab has strong national as well as international collaborations and based in the heart of Brussels, offers plenty of possibilities for collaboration with industry. Founded in 1983, by Luc Steels, the AI lab became the first Artificial Intelligence lab on European mainland. The lab is active in a variety of AI domains, including Evolution of language, machine learning, multi-agent systems, reinforcement learning, evolutionary systems and bioinformatics.

Bribery in elections is an important problem in computational social choice theory. However, bribery with money is often illegal in elections. Motivated by this, we introduce the notion of frugal bribery and formulate two new pertinent computational problems which we call Frugal-bribery and Frugal- $bribery to capture bribery without money in elections. In the proposed model, the briber is frugal in nature and this is captured by her inability to bribe votes of a certain kind, namely, non-vulnerable votes. In the Frugal-bribery problem, the goal is to make a certain candidate win the election by changing only vulnerable votes. In the Frugal-{dollar}bribery problem, the vulnerable votes have prices and the goal is to make a certain candidate win the election by changing only vulnerable votes, subject to a budget constraint of the briber. We further formulate two natural variants of the Frugal-{dollar}bribery problem namely Uniform-frugal-{dollar}bribery and Nonuniform-frugal-{dollar}bribery where the prices of the vulnerable votes are, respectively, all the same or different. We study the computational complexity of the above problems for unweighted and weighted elections for several commonly used voting rules. We observe that, even if we have only a small number of candidates, the problems are intractable for all voting rules studied here for weighted elections, with the sole exception of the Frugal-bribery problem for the plurality voting rule. In contrast, we have polynomial time algorithms for the Frugal-bribery problem for plurality, veto, k-approval, k-veto, and plurality with runoff voting rules for unweighted elections. However, the Frugal-{dollar}bribery problem is intractable for all the voting rules studied here barring the plurality and the veto voting rules for unweighted elections.

In August 2011, Igor Labutov and Jason Yosinski, two PhD students at Cornell University, let a pair of chat bots, called Alan and Sruthi, talk to each other online. Starting with a simple greeting, the one-and-a-half-minute dialogue quickly escalated into an argument about what Alan and Sruthi had just said, whether they were robots, and about God [1]. The first ever conversation between two simple artificial intelligence agents ended in a conflict. A bot, or software agent, is a computer program that is persistent, autonomous, and reactive [2,3]. Bots are defined by programming code that runs continuously and can be activated by itself.

Wondering who will win the 2017 Oscars? Instead of turning to industry experts, film critics, or polls, you can try something else this year: Artificial intelligence. A startup called Unanimous A.I. has been making predictions--like who will win the Superbowl, March Madness, US presidential debates, the Kentucky Derby--for the last two years. It uses a software platform called UNU to assemble people at their computers, who make a real-time prediction together. UNU's algorithm is built to harness the concept of "swarm" intelligence--the power of a group to make an intelligent, collective decision.

Earlier this week I attended an interesting session on Artificial Intelligence (AI) and computing organised by Microsoft. The highlight of the session was the conversation between Nandan Nilekani and Satya Nadella, where the Microsoft CEO mentioned that his top three focus areas for the future would be cloud and AI, Agents (bots) and Natural Language Processing (NLP) and Augmented Reality (AR). The session had a number of very interesting demos that utilised Microsoft's cognitive toolkit to address real world issues. While the indiscriminate use of the term AI did distress me, I was heartened to hear Satya Nadella caution everyone to be realistic about AI and its applicability. We are at the dawn of a new age right now, somewhat akin to what people must have felt when the industrial revolution started.

In the future, it's likely that many aspects of human society will be controlled -- either partly or wholly -- by artificial intelligence. AI computer agents could manage systems from the quotidian (e.g., traffic lights) to the complex (e.g., a nation's whole economy), but leaving aside the problem of whether or not they can do their jobs well, there is another challenge: will these agents be able to play nice with one another? What happens if one AI's aims conflict with another's? Will they fight, or work together? Google's AI subsidiary DeepMind has been exploring this problem in a new study published today.

Algorithms for equilibrium computation generally make no attempt to ensure that the computed strategies are understandable by humans. For instance the strategies for the strongest poker agents are represented as massive binary files. In many situations, we would like to compute strategies that can actually be implemented by humans, who may have computational limitations and may only be able to remember a small number of features or components of the strategies that have been computed. We study poker games where private information distributions can be arbitrary. We create a large training set of game instances and solutions, by randomly selecting the information probabilities, and present algorithms that learn from the training instances in order to perform well in games with unseen information distributions. We are able to conclude several new fundamental rules about poker strategy that can be easily implemented by humans.

Google's artificial intelligence subsidiary DeepMind is pitting AI agents against one another to test how they interact with each other and how they would react in various "social dilemmas". In a new study, researchers said they used two video games – Wolfpack and Gathering – to examine how AI agents change the way they behave based on the environment and situation they are in using social sciences and game theory principles. "The question of how and under what circumstances selfish agents cooperate is one of the fundamental questions in the social sciences," DeepMind researchers wrote in a blog post. "One of the simplest and most elegant models to describe this phenomenon is the well-known game of Prisoner's Dilemma from game theory." This well-known principle is based on the scenario where two arrested suspects jointly accused of a crime are questioned separately.

As intelligent agents become more autonomous, sophisticated, and prevalent, it becomes increasingly important that humans interact with them effectively. Machine learning is now used regularly to acquire expertise, but common techniques produce opaque content whose behavior is difficult to interpret. Before they will be trusted by humans, autonomous agents must be able to explain their decisions and the reasoning that produced their choices. We will refer to this general ability as explainable agency. This capacity for explaining decisions is not an academic exercise. When a self-driving vehicle takes an unfamiliar turn, its passenger may desire to know its reasons. When a synthetic ally in a computer game blocks a player's path, he may want to understand its purpose. When an autonomous military robot has abandoned a high-priority goal to pursue another one, its commander may request justification. As robots, vehicles, and synthetic characters become more self-reliant, people will require that they explain their behaviors on demand. The more impressive these agents' abilities, the more essential that we be able to understand them.