Energy consumption in commercial and educational buildings is impacted by group activities such as meetings, workshops, classes and exams, and can be reduced by scheduling these activities to take place at times and locations that are favorable from an energy standpoint. This paper improves on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. To scale up to realistic problem sizes, we embed this MILP model into a large neighbourhood search (LNS). We obtain substantial energy reduction in comparison with occupancy-based HVAC control using arbitrary schedules or using schedules obtained by existing heuristic energy-aware scheduling approaches.

Laughter is an important social signal which may have various communicative functions (Chapman 1983). Humans laugh at humorous stimuli or to mark their pleasure when receiving praised statements (Provine 2001); they also laugh to mask embarrassment (Huber and Ruch 2007) or to be cynical. Laughter can also act as social indicator of ingroup belonging (Adelswärd 1989); it can work as speech regulator during conversation (Provine 2001); it can also be used to elicit laughter in interlocutors as it is very contagious (Provine 2001). Endowing machines with laughter capabilities is a crucial challenge to develop virtual agents and robots able to act as companions, coaches, or supporters in a more natural manner. However, so far, few attempts have been made to model and implement laughter for virtual Figure 1: the architecture of our laughing agent.

Our method automatically generates realistic nonverbal performances for virtual characters to accompany spo- ken utterances. It analyses the acoustic, syntactic, se- mantic and rhetorical properties of the utterance text and audio signal to generate nonverbal behavior such as such as head movements, eye saccades, and novel gesture animations based on co-articulation.

In this paper we propose a wrapper based PSO method for gene selection in microarray datasets, where we gradually refine the feature (gene) space from a very coarse level to a fine grained one, by reducing the gene set at each step of the algorithm. We use the linear support vector machine weight vector to serve as the initial gene pool selection. In addition, we also examine integration of other filter based ranking methods with our proposed approach. Experiments on publicly available datasets, Colon, Leukemia and T2D show that our approach selects only a very small subset of genes while yielding substantial improvements in accuracy over state-of-the-art evolutionary methods.

Cooperative Intelligent Transportation Systems (C-ITS) are complex systems well-suited to a multi-agent modeling. We propose a multi-agent based modeling of a C-ITS, that couples 3 dynamics (physical, informational and control dynamics) in order to ensure a smooth cooperation between non cooperative and cooperative vehicles, that communicate with each other (V2V communication) and the infrastructure (I2V and V2I communication). We present our multi-agent model, tested through simulations using real traffic data and integrated into our extension of the Multi-model Open-source Vehicular-traffic SIMulator (MovSim).

It is known that we can aggregate the opinions of different agents to find high-quality solutions to complex problems. However, choosing agents to form a team is still a great challenge. Moreover, it is essential to use a good aggregation methodology in order to unleash the potential of a given team in solving complex problems. In my thesis, I present two different novel models to aid in the team formation process. Moreover, I propose a new methodology for extracting rankings from existing agents. I show experimental results both in the Computer Go domain and in the building design domain.

In a heterogeneous multiagent system it can be useful to have knowledge about the different types of agents in the system. Agent modeling develops agent models based on interactions between agents, then predicts agent actions. This approach is effective in small domains but does not scale well. We develop an approach where an agent can learn using an abstract model identification or stereotype rather than an explicit and unique model for each agent. We associate each agent with a stereotype and learn a policy incorporating this knowledge. The benefits of this approach are that it is simple, scalable, and degrades gracefully with misidentification.

My dissertation research focuses on the application of hierarchical learning and heuristics based on social signals to solve challenges inherent to enabling human-robot collaboration. I approach this problem through advancing the state of the art in building hierarchical task representations, multi-agent task-level planning, and learning assistive behaviors from demonstration.

In the proposed thesis, we study Distributed Constraint Optimization Problems (DCOPs), which are problems where several agents coordinate with each other to optimize a global cost function. The use of DCOPs has gained momentum, due to their capability of addressing complex and naturally distributed problems. A majority of the work in DCOP addresses the resolution problem by detaching the model from the resolution process, where they assume that each agent controls exclusively one variable of the problem (Burke et al. 2006). This assumption often is not reflected in the model specifications, and may lead to inefficient communication requirements. Another limitation of current DCOP resolution methods is their inability to capitalize on the presence of structural information, which may allow incoherent/unnecessary data to reticulate among the agents (Yokoo 2001). The purpose of the proposed dissertation is to study how to adapt and integrate insights gained from centralized solving techniques in order to enhance DCOP performance and scalability, enabling their use for the resolution of real-world complex problems. To do so, we hypothesize that one can exploit the DCOP structure in both problem modeling and problem resolution phases.

We study a self-organized collective decision-making strategy to solve a site-selection problem using a swarm of simple robots. Robots can only move forward or turn in place; sense the intensity of the ambient light; and exchange 3-byte messages with peers in a limited range. The goal of the swarm is to collectively decide which of the sites available in the environment is the best candidate site. We define a distributed and iterative decision-making strategy: robots explore the available options, determine the options' qualities, decide autonomously which option to take, and communicate their decision to neighboring robots. We study the effectiveness and robustness of the proposed strategy using a swarm of 100 Kilobots and we focus on the impact of the neighborhood size over the dynamics of the system.