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We introduce a non-parametric hierarchical Bayesian approach for open-ended 3D object categorization, named the Local Hierarchical Dirichlet Process (Local-HDP). This method allows an agent to learn independent topics for each category incrementally and to adapt to the environment in time. Hierarchical Bayesian approaches like Latent Dirichlet Allocation (LDA) can transform low-level features to high-level conceptual topics for 3D object categorization. However, the efficiency and accuracy of LDA-based approaches depend on the number of topics that is chosen manually. Moreover, fixing the number of topics for all categories can lead to overfitting or underfitting of the model. In contrast, the proposed Local-HDP can autonomously determine the number of topics for each category. Furthermore, an inference method is proposed that results in a fast posterior approximation. Experiments show that Local-HDP outperforms other state-of-the-art approaches in terms of accuracy, scalability, and memory efficiency with a large margin.

The first European Rhinology Research Forum organized by the European Forum for Research and Education in Allergy and Airway Diseases (EUFOREA) was held in the Royal Academy of Medicine in Brussels on 17th and 18th November 2016, in collaboration with the European Rhinologic Society (ERS) and the Global Allergy and Asthma European Network (GA2LEN). One hundred and thirty participants (medical doctors from different specialties, researchers, as well as patients and industry representatives) from 27 countries took part in the multiple perspective discussions including brainstorming sessions on care pathways and research needs in rhinitis and rhinosinusitis. The debates started with an overview of the current state of the art, including weaknesses and strengths of the current practices, followed by the identification of essential research needs, thoroughly integrated in the context of Precision Medicine (PM), with personalized care, prediction of success of treatment, participation of the patient and prevention of disease as key principles for improving current clinical practices. This report provides a concise summary of the outcomes of the brainstorming sessions of the European Rhinology Research Forum 2016.

Making a suitable career choice is a difficult task. Every year, many adolescents prematurely end their studies, commonly citing ‘having made the wrong choice’ as the main reason. This is a problem, both for the adolescents making these choices, and for society, which bears at least part of the cost of higher education. A thorough understanding of how adolescents make these career choices is essential to identifying the factors responsible for why the wrong choices are often made. Identity development theory emphasizes the role of exploration in career choice, but neglects many of the micro-level processes likely to play an important role. Similarly, traditional decision theory often focuses on optimization of choice, thereby neglecting the cognitive mechanisms that may explain deviations from optimal choice. Here, we present a novel computational approach to modelling long-term decision making. We combine elements of the macro-level theory on identity development with a firm rooting in micro-level cognitive processes. Specifically, we model decision making as an iterative process in which individuals can explore new options or more deeply investigate options that are already under consideration. The output of our model allows us to analyze how the quality of decisions depends on various factors, such as aspiration levels, the tendency to explore new options, and the ability to judge the fit of an option with one’s interests and capabilities. We present some preliminary results that already show our approach can lead to surprising conclusions, encouraging further development of this model in the future.

The first European Rhinology Research Forum organized by the European Forum for Research and Education in Allergy and Airway Diseases (EUFOREA) was held in the Royal Academy of Medicine in Brussels on 17th and 18th November 2016, in collaboration with the European Rhinologic Society (ERS) and the Global Allergy and Asthma European Network (GA2LEN). One hundred and thirty participants (medical doctors from different specialties, researchers, as well as patients and industry representatives) from 27 countries took part in the multiple perspective discussions including brainstorming sessions on care pathways and research needs in rhinitis and rhinosinusitis. The debates started with an overview of the current state of the art, including weaknesses and strengths of the current practices, followed by the identification of essential research needs, thoroughly integrated in the context of Precision Medicine (PM), with personalized care, prediction of success of treatment, participation of the patient and prevention of disease as key principles for improving current clinical practices. This report provides a concise summary of the outcomes of the brainstorming sessions of the European Rhinology Research Forum 2016.

Precise analysis of agent-based model (ABM) outputs can be a challenging and even onerous endeavor. Multiple runs or Monte Carlo sampling of one's model (for the purposes of calibration, sensitivity or parameter-outcome analysis) often yield a large set of trajectories or state transitions which may, under certain measurements, characterize the model's behavior. These temporal state transitions can be represented as a directed graph (or network) which is then amenable to network analytic and graph theoretic measurements. Building on strategies of aggregating model outputs from multiple runs into graphs, we devise a temporally-constrained graph aggregating state changes from runs and examine its properties in order to characterize the behavior of a land-use change ABM, the RHEA model. Features of these graphs are transformed into measures of complexity which in turn vary with different parameter or experimental conditions. This approach provides insights into the model behavior beyond traditional statistical analysis. We find that increasing the complexity in our experimental conditions can ironically decrease the complexity in the model behavior.

How can one influence the behavior of others? What is a good persuasion strategy? It is obviously of great importance to determine what information best to provide and also how to convey it. To delineate how and when manipulation of others can be successful, the first part of this chapter reviews basic findings of decision and game theory on models of strategic communication. But there is also a social aspect to manipulation, concerned with determining who we should address so as best to promote our opinion in a larger group or society as a whole. The second half of this chapter therefore looks at a novel extension of DeGroot’s [19] classical model of opinion dynamics that allows agents to strategically influence some agents more than others. This side-by-side investigation of psychological and social aspects enables us to reflect on the general question what a good manipulation strategy is. We submit that successful manipulation requires exploiting critical weaknesses, such as limited capability of strategic reasoning, limited awareness, susceptibility to cognitive biases or to potentially indirect social pressure.

This piece is not a paper reporting on original research, but rather a slightly expanded write-up of some notes for a concluding discussion at the 2012 Workshop on ‘Modeling Strategic Reasoning’ at the Lorentz Center in Leiden, an interdisciplinary meeting on the importance of strategies in many fields, from game theory to linguistics, computer science, and cognitive science, that was the incubator for the present volume on the logic-based analysis of strategies and how we reason with, and about them. My modest purpose here is to highlight a few general, somewhat unresolved, decision points about this proposed program that seemed to resonate with the audience at the Workshop, but that may also present food for thought to a more general reader of this book. The emphasis in the presentation that follows is on logic, a view of strategies that figures prominently in my own work on logic and games, cf. [9]. Still, there are certainly other equally viable and illuminating formal viewpoints on the study of strategies, coming, for instance, from automata theory or dynamical systems, cf. [16, 31].

Viewing the way society has defined its rules and mechanisms as “social software”, we want to understand how people behave given their understanding of the societal rules and given their wish to further their interest as they conceive it, and how social mechanisms should be designed to suit people furthering their interest as they conceive it. This chapter is written from the perspective of strategic game theory, and uses strategic game scenarios and game transformations to analyze societal mechanisms.

Social procedures that have algorithmic aspects can often be improved by redesign. This holds for voting and other peaceful decision making procedures, for match-making, for auctioning, for fair division of estates, and for many procedures of distributive justice. The algorithmic aspects can be analyzed with formal methods. The term “social software” was coined by Rohit Parikh (2002) for the emerging interdisciplinary enterprise that is concerned with the design and analysis of algorithms that regulate social processes. Such analysis and (re-)design uses methods from logic, game theory and theoretical computer science. The goals of research in formal approaches to social procedures are modeling social situations, developing theories of correctness, and (re-)designing social procedures. Logic, game theory and computer science are not the only disciplines that have something to say about social mechanisms. Such mechanisms are also an object of study in voting theory, in auction theory, in social choice theory, in mechanism design theory, and in algorithmic game theory. Multi-agent interaction at a more abstract level is studied in artificial intelligence and distributed computing, so all these disciplines have something to say about the formal analysis of social interaction.

Ancient Chinese literature on strategic theory goes back to ideas proposed almost 3000 years ago, but which were first written down during the late Spring and Autumn period and the Warring States period (around 500 BC). The most famous work is the Art of War from Sun Tzu. While Sun Tzu extensively recommends to prepare before going to war and to study and exploit the strengths and weaknesses of your opponent, he doesn't explicitly mentions to investigate the possible reasoning of your opponent. There is no visible trace of Theory-of-Mind reasoning in his work. More recent literature, like the famous classic historic novel Three Kingdoms (believed to have been written during the 14th Century), which describes events during the period 168 to 280 AD, contains instances of Theory-of-Mind reasoning. The question thus arises when the ancient Chinese did invent Theory-of-Mind reasoning as a concept, and why they seem not to have used it in their extensive Strategic theories.

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We study structural properties of a turn-based game called the Marble Drop Game, which is an experimental paradigm designed to investigate higher-order social reasoning. We show that the cognitive complexity of game trials, measured with respect to reaction time, can be predicted by looking at the structural properties of the game instances. In order to do this, we define complexity measures of finite dynamic two-player games based on the number of alternations between the game players and on the pay-off structure. Our predictions of reaction times and reasoning strategies, based on the theoretical analysis of complexity of Marble Drop game instances, are compared to subjects' actual reaction times. This research illustrates how formal methods of logic and computer science can be used to identify the inherent complexity of cognitive tasks. Such analyses can be located between Marr's computational and algorithmic levels.

International audience; We model lying as a communicative act changing the beliefs of the agents in a multi-agent system. With Augustine, we see lying as an utterance believed to be false by the speaker and uttered with the intent to deceive the addressee. The deceit is successful if the lie is believed after the utterance by the addressee. This is our perspective. Also, as common in dynamic epistemic logics, we model the agents addressed by the lie, but we do not (necessarily) model the speaker as one of those agents. This further simplifies the picture: we do not need to model the intention of the speaker, nor do we need to distinguish between knowledge and belief of the speaker: he is the observer of the system and his beliefs are taken to be the truth by the listeners. We provide a sketch of what goes on logically when a lie is communicated. We present a complete logic of manipulative updating, to analyse the effects of lying in public discourse. Next, we turn to the study of lying in games. First, a game-theoretical analysis is used to explain how the possibility of lying makes games such as Liar’s Dice interesting, and how lying is put to use in optimal strategies for playing the game. This is the opposite of the logical manipulative update: instead of always believing the utterance, now, it is never believed. We also give a matching logical analysis for the games perspective, and implement that in the model checker DEMO. Our running example of lying in games is the game of Liar’s Dice.