We introduce epistemic quantified boolean logic (EQBL), an extension of propositional epistemic logic with quantification over propositions. We show that EQBL can express relevant properties about agents’ knowledge in multi-agent contexts, such as “agent a knows as much as agent b”. We analyse the expressiveness of EQBL through a translation into monadic second-order logic, and provide completeness results w.r.t. various classes of Kripke frames. Finally, we prove that model checking EQBL is PSPACE-complete. Thus, the complexity of model checking EQBL is no harder than for (non-modal) quantified boolean logic.

Many countries like Singapore are planning to introduce Electric Vehicles (EVs) to replace traditional vehicles to reduce air pollution and improve energy efficiency. The rapid development of EVs calls for efficient deployment of charging stations both for the convenience of EVs and maintaining the efficiency of the road network. Unfortunately, existing work makes unrealistic assumption on EV drivers' charging behaviors and focus on the limited mobility of EVs. This paper studies the Charging Station PLacement (CSPL) problem, and takes into consideration 1) EV drivers' strategic behaviors to minimize their charging cost, and 2) the mutual impact of EV drivers' strategies on the traffic conditions of the road network and service quality of charging stations. We first formulate the CSPL problem as a bilevel optimization problem, which is subsequently converted to a single-level optimization problem by exploiting structures of the EV charging game played by EV drivers. Properties of CSPL problem are analyzed and an algorithm called OCEAN is proposed to compute the optimal allocation of charging stations. We further propose a heuristic algorithm OCEAN-C to speed up OCEAN. Experimental results show that the proposed algorithms significantly outperform baseline methods.

We propose new mechanisms that can be used by a demand response aggregator to flexibly shift the charging of electric vehicles (EVs) to times where cheap but intermittent renewable energy is in high supply. Here, it is important to consider the constraints and preferences of EV owners, while eliminating the scope for strategic behaviour. To achieve this, we propose, for the first time, a generic class of incentive mechanisms for settings with both varying marginal electricity costs and multidimensional preferences. We show these are dominant strategy incentive compatible, i.e., EV owners are incentivised to report their constraints and preferences truthfully. We also detail a specific instance of this class, show that it achieves ≈98% of the optimal in realistic scenarios and demonstrate how it can be adapted to trade off efficiency with profit.

In cooperation with a social startup, FoodBank Local, we have been helping Food Bank Australia develop technologies We study an online model of fair division designed to operate more effectively. So far, this has involved building to capture features of a real world charity problem.

Information Aesthetics is an emerging sub-field of Data Visualization that aims to engage the viewers and lure them into decode the visualization. The introduction of self-organising systems can aid the creation of these visual representations through the exploration of emergent patterns. In this paper, we apply a swarm based system as a method to create emergent visualizations of data that convey meaningful information in an inciting way, exploring the boundaries between Data Visualization and Information Aesthetics. The approach is used to visually convey the consumption patterns in 729 Portuguese hypermarkets over the course of two years. The analysis of the experimental results focuses on the ability of the emergent visualizations to communicate information while engaging the viewer with organic visuals.

Goal-driven autonomy (GDA) agents reason about goals while introspectively examining if their course of action matches their expectations. Many GDA agents adopt a hierarchical planning model to generate plans but limit reasoning with expectations to individual actions or projecting the expected state. In this paper we present a relaxation of this limitation. Taking advantage of hierarchical planning principles, our GDA agent elicits expectations that not only validate the next action but the overall plan trajectory without requiring validation against the complete state. We report on (1) a formalization of GDA's expectations that covers trajectories, (2) an implementation of these ideas and (3) benchmarking on two domains used in the GDA literature.

We introduce the study of adversarial effects on wisdom of the crowd phenomena. In particular, we examine the ability of an adversary to influence a social network so that the majority of nodes are convinced by a falsehood, using its power to influence a certain fraction, μ < 0.5 of N experts. Can a bad restaurant make a majority of the overall network believe in the quality of that restaurant by misleading a certain share of food critics into believing its food is good, and use the influence of those experts to make a majority of the overall network to believe in the quality of that restaurant? We are interested in providing an agent, which does not necessarily know the graph structure nor who the experts are, to determine the true value of a binary property using a simple majority. We prove bounds on the social graph's maximal degree, which ensure that with a high probability the adversary will fail (and the majority vote will coincide with the true value) when it can choose who the experts are, while each expert communicates the true value with probability p > 0.5. When we examine expander graphs as well as random graphs we prove such bounds even for stronger adversaries, who are able to pick and choose not only who the experts are, but also which ones of them would communicate the wrong values, as long as their proportion is 1-p. Furthermore, we study different propagation models and their effects on the feasibility of obtaining the true value for different adversary types.

We consider revenue negotiation problems in iterative settings. In our model, a group of agentshas some initial resources, used in order to generate revenue. Agents must agree on some way of dividing resources, but there’s a twist. At every time-step, the revenue shares received at time t are agent resources at time t + 1, and the game is repeated. The key issue here is that the way resources are shared has a dramatic effect on long term social welfare, so in order to maximize individual long-term revenue one must consider the welfare of others, a behavior not captured by other models of cooperation and bargaining. Our work focuses on homogeneous production functions. We identify conditions that ensure that the socially optimal outcome is an epsilon-Nash equilibrium. We apply our results to some families of utility functions, and discuss their strategic implications.

They pinpoint families of voting rules that exhibit robustness: they are accurate in the limit with respect to a wide Classic social choice theory assumes that votes are range of noise models, which govern the way noisy votes are independent (but possibly conditioned on an underlying generated, given the ground truth [Caragiannis et al., 2013; objective ground truth). This assumption 2014]. is unrealistic in settings where the voters are connected While these results are promising, they rely on a crucial via an underlying social network structure, modeling assumption: votes are independent. This assumption as social interactions lead to correlated votes. We is clearly satisfied in some settings -- when votes are establish a general framework -- based on random submitted by computer Go programs [Jiang et al., 2014], say.

Voting is commonly used as a method for aggregating information in crowdsourcing and human computation. In many settings, one would like to use voting rules which can be efficiently elicited, preserve voter privacy, and are robust to strategic manipulation. In this paper, we give algorithms which elicit approximate winners in a way which provably satisfies all three of these requirements simultaneously. Our results hold for tournament voting rules, which we define to be the voting rules which can be expressed solely as a function of the table of pairwise comparisons containing the number of voters preferring one candidate to another. Tournament voting rules include many common voting rules such as the Borda, Copeland, Maximin, Nanson, Baldwin, Kemeny-Young, Ranked Pairs, Cup, and Schulze voting rules. Our results significantly expand the set of voting rules for which efficient elicitation was known to be possible and improve the known approximation factors for epsilon-strategyproof voting in the regime where the number of candidates is large.