Electric vehicles will play a crucial role in balancing the future electrical grid, which is complicated by many intermittent renewable energy sources. We developed an algorithm that determines for a fleet of electric vehicles, which EV at what price and location to commit to the operating reserve market to either absorb excess capacity or provide electricity during shortages (vehicle-2-grid). The algorithm takes the value of immobility into account by using carsharing fees as a reference point. A virtual power plant autonomously replaces cars that are committed to the operating reserves and are then rented out, with other idle cars to pool the risks of uncertainty. We validate our model with data from a free float carsharing fleet of 500 electric vehicles. An analysis of expected future developments (2015, 2018, and 2022) in operating reserve demand and battery costs yields that the gross profits for a carsharing operator increase between 7-12% with a negligible decrease in car availability (<0.01%).

Predicting the affective valence of unknown multi-word expressions is key for concept-level sentiment analysis. AffectiveSpace 2 is a vector space model, built by means of random projection, that allows for reasoning by analogy on natural language con- cepts. By reducing the dimensionality of affec- tive common-sense knowledge, the model allows semantic features associated with concepts to be generalized and, hence, allows concepts to be intu- itively clustered according to their semantic and affective relatedness. Such an affective intuition (so called because it does not rely on explicit fea- tures, but rather on implicit analogies) enables the inference of emotions and polarity conveyed by multi-word expressions, thus achieving efficient concept-level sentiment analysis.

The RoboCup@Home league has been founded in 2006with the idea to drive research in AI and related fieldstowards autonomous and interactive robots that copewith real life tasks in supporting humans in everday life.The yearly competition format establishes benchmarkingas a continuous process with yearly changes insteadof a single challenge. We discuss the current state andfuture perspectives of this endeavor.

The cost-optimal track of the international planning competition in 2014 has seen an unexpected outcome. Different to the precursing competition in 2011, where explicit-state heuristic search planning scored best, advances in the state-set exploration with BDDs showed a significant lead. In this paper we review the outcome of the competition, briefly looking into the internals of the competing systems.

Knowledge representation and reasoning (KR) stems ing the representation and computational management of from a deep tradition in logic. In particular, it aims at building knowledge. The first KR conference was held 25 years ago systems that know about their world and are able to act in in 1989. The last KR edition KR 2014 was the 14th and was an informed way in it, as humans do. A crucial part of these held 25th year of the first KR conference.

Narrative generation represents an application domain for AI planning where plan quality is related to properties such as shape of plan trajectory. In our work we have developed a plan-based approach to narrative generation that uses character relationships as a key determinant in controlling plan shape (relationships are key in genres such as serial dramas and soaps). Our approach is implemented in a demonstration Interactive Narrative, called NetworkING, set in the medical drama genre. The system features a user-friendly mechanism for specifying relationships between virtual characters, via a social network and real-time visualisation of generated narratives on a 3D stage.

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.

This paper overviews our application of state-of-the-art automated planning algorithms to real mobile robots performing an autonomous construction task, a domain in which robots are prone to faults. We describe how embracing these faults leads to better representations and smarter planning, allowing robots with limited precision to avoid catastrophic failures and succeed in intricate constructions.

We demonstrate an approach to predict latent personal attributes including user demographics, online personality, emotions and sentiments from texts published on Twitter. We rely on machine learning and natural language processing techniques to learn models from user communications. We first examine individual tweets to detect emotions and opinions emanating from them, and then analyze all the tweets published by a user to infer latent traits of that individual. We consider various user properties including age, gender, income, education, relationship status, optimism and life satisfaction. We focus on Ekman’s six emotions: anger, joy, surprise, fear, disgust and sadness. Our work can help social network users to understand how others may perceive them based on how they communicate in social media, in addition to its evident applications in online sales and marketing, targeted advertising, large scale polling and healthcare analytics.