Evacuation planning is a critical aspect of disaster preparedness and response to minimize the number of people exposed to a threat. Controlled evacuations aim at managing the flow of evacuees as efficiently as possible and have been shown to produce significant benefits compared to self-evacuations. However, existing approaches do not capture the delays introduced by diverging and crossing evacuation routes, although evidence from actual evacuations highlights that these can lead to significant congestion. This paper introduces the concept of convergent evacuation plans to tackle this issue. It presents a MIP model to obtain optimal convergent evacuation plans which, unfortunately, does not scale to realistic instances. The paper then proposes a two-stage approach that separates the route design and the evacuation scheduling. Experimental results on a real case study show that the two-stage approach produces better primal bounds than the MIP model and is two orders of magnitude faster; It also produces dual bounds stronger than the linear relaxation of the MIP model. Finally, simulations of the evacuation demonstrate that convergent evacuation plans outperform existing approaches for realistic driver behaviors.

Stackelberg security games have been widely deployed to protect real-word assets. The main solution concept there is the Strong Stackelberg Equilibrium (SSE), which optimizes the defender's random allocation of limited security resources. However, solely deploying the SSE mixed strategy has limitations. In the extreme case, there are security games where the defender is able to defend all the assets ``almost perfectly" at the SSE, but she still sustains significant loss. In this paper, we propose an approach for improving the defender's utility in such scenarios. Perhaps surprisingly, our approach is to strategically reveal to the attacker information about the sampled pure strategy. Specifically, we propose a two-stage security game model, where in the first stage the defender allocates resources and the attacker selects a target to attack, and in the second stage the defender strategically reveals local information about that target, potentially deterring the attacker's attack plan. We then study how the defender can play optimally in both stages. We show, theoretically and experimentally, that the two-stage security game model allows the defender to gain strictly better utility than SSE.

Team formation is a core problem in AI. Remarkably, little prior work has addressed the problem of mechanism design for team formation, accounting for the need to elicit agents' preferences over potential teammates. Coalition formation in the related hedonic games has received much attention, but only from the perspective of coalition stability, with little emphasis on the mechanism design objectives of true preference elicitation, social welfare, and equity. We present the first formal mechanism design framework for team formation, building on recent combinatorial matching market design literature. We exhibit four mechanisms for this problem, two novel, two simple extensions of known mechanisms from other domains. Two of these (one new, one known) have desirable theoretical properties. However, we use extensive experiments to show our second novel mechanism, despite having no theoretical guarantees, empirically achieves good incentive compatibility, welfare, and fairness.

We study strong equilibria in symmetric capacitated cost-sharing games. In these games, a graph with designated source s and sink t is given, and each edge is associated with some cost. Each agent chooses strategically an s-t path, knowing that the cost of each edge is shared equally between all agents using it. Two variants of cost-sharing games have been previously studied: (i) games where coalitions can form, and (ii) games where edges are associated with capacities; both variants are inspired by real-life scenarios. In this work we combine these variants and analyze strong equilibria (profiles where no coalition can deviate) in capacitated games. This combination gives rise to new phenomena that do not occur in the previous variants. Our contribution is two-fold. First, we provide a topological characterization of networks that always admit a strong equilibrium. Second, we establish tight bounds on the efficiency loss that may be incurred due to strategic behavior, as quantified by the strong price of anarchy (and stability) measures. Interestingly, our results are qualitatively different than those obtained in the analysis of each variant alone, and the combination of coalitions and capacities entails the introduction of more refined topology classes than previously studied.

Roughly speaking, Interdependent Defense (IDD) games, previously proposed, model the situation where an attacker wants to cause as much damage as possible to a network by attacking one of the sites in the network. Each site must make an investment decision regarding security to protect itself against a direct or indirect attack, the latter due to potential transfer-risk from an unprotected neighboring site. The work introducing IDD games discusses potential applications to model the essence of real-world scenarios such as the 2006 transatlantic aircraft plot. In this paper, our focus is the study of the problem of computing a Nash Equilibrium (NE) in IDD games. We show that an efficient algorithm to determine whether some attacker’s strategy can be a part of a NE in an instance of IDD games is unlikely to exist. Yet, we provide a dynamic programming algorithm to compute an approximate NE when the graph/network structure of the game is a directed tree with a single source, and show that it is an FPTAS. We also introduce an improved heuristic to compute an approximate NE on arbitrary graph structures. Our experiments show that our heuristic is more efficient, and provides better approximations, than best-response-gradient dynamics for the case of Internet games, a class of games introduced and studied in the original work on IDD games.

We describe an optimization-based approach for automatically creating well-edited movies from a 3D animation. While previous work has mostly focused on the problem of placing cameras to produce nice-looking views of the action, the problem of cutting and pasting shots from all available cameras has never been addressed extensively. In this paper, we review the main causes of editing errors in literature and propose an editing model relying on a minimization of such errors. We make a plausible semi-Markov assumption, resulting in a dynamic programming solution which is computationally efficient. We also show that our method can generate movies with different editing rhythms and validate the results through a user study. Combined with state-of-the-art cinematography, our approach therefore promises to significantly extend the expressiveness and naturalness of virtual movie-making.

In this paper, we consider the problem of energy disaggregation, i.e., decomposing a whole home electricity signal into its component appliances. We propose a new supervised algorithm, which in the learning stage, automatically extracts signature consumption patterns of each device by modeling the device as a mixture of dynamical systems. In order to extract signature consumption patterns of a device corresponding to its different modes of operation, we define appropriate dissimilarities between energy snippets of the device and use them in a subset selection scheme, which we generalize to deal with time-series data. We then form a dictionary that consists of extracted power signatures across all devices. We cast the disaggregation problem as an optimization over a representation in the learned dictionary and incorporate several novel priors such as device-sparsity, knowledge about devices that do or do not work together as well as temporal consistency of the disaggregated solution. Real experiments on a publicly available energy dataset demonstrate that our proposed algorithm achieves promising results for energy disaggregation.

We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, arrange one-time rides at short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system modelling such problem as a graph constrained coalition formation (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the applicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to -36.22%). Moreover, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds).

Applications in sustainability domains such as in energy, transportation, and natural resource and environment monitoring, increasingly use sensors for collecting data and sending it back to centrally located processing nodes. While data can usually be collected by the sensors at a very high speed, in many cases, it can not be sent back to central nodes at a frequency that is required for fast and real-time modeling and decision-making. This may be due to physical limitations of the transmission networks, or due to consumers limiting frequent transmission of data from sensors located at their premises for security and privacy concerns. We propose a novel solution to the problem of making short term predictions in absence of real-time data from sensors. A key implication of our work is that by using real-time data from only a small subset of influential sensors, we are able to make predictions for all sen- sors. We evaluated our approach with a large real-world electricity consumption data collected from smart meters in Los Angeles and the results show that between prediction horizons of 2 to 8 hours, despite lack of real time data, our influence model outperforms the baseline model that uses real-time data. Also, when using partial real-time data from only ≈ 7% influential smart meters, we witness prediction error increase by only ≈ 0.5% over the baseline, thus demonstrating the usefulness of our method for practical scenarios.

Hippocampal place cells and entorhinal grid cells have been hypothesized to be able to form map-like spatial representation of the environment, namely cognitive map. In most prior approaches, either neural network methods or only hippocampal models are used for building cognitive maps, lacking biological fidelity to the entorhinal-hippocampal system. This paper presents a novel computational model to build cognitive maps of real environments using both place cells and grid cells. The proposed model includes two major components: (1) A competitive Hebbian learning algorithm is used to select velocity-coupled grid cell population activities, which path-integrate self-motion signals to determine computation of place cell population activities; (2) Visual cues of environments are used to correct the accumulative errors intrinsically associated with the path integration process. Experiments performed on a mobile robot show that cognitive maps of the real environment can be efficiently built. The proposed model would provide an alternative neuro-inspired approach for robotic mapping, navigation and localization.