Koen Hindriks

AAMAS Conference 2023 Conference Paper

Emotion Contagion in Agent-based Simulations of Crowds: A Systematic Review

• Erik van Haeringen
• Charlotte Gerritsen
• Koen Hindriks

Emotions are known to spread among people, a process called emotion contagion. Both positive and negative emotions are believed to be contagious, but the mass spread of negative emotions has attracted the most attention due to its danger to society. The use of agent-based techniques to simulate emotion contagion in crowds has grown over the last decade and a range of contagion mechanisms and applications have been considered. With this review we aim to give a comprehensive overview of agent-based methods to implement emotion contagion in crowd simulations. We took a systematic approach and collected studies from Web of Science, Scopus, IEEE and ACM that propose agent-based models that include a process of emotion contagion in crowds. We classify the models in three categories based on the mechanism of emotion contagion and analyse the contagion mechanism, application and findings of the studies. Additionally, a broad overview is given of other agent characteristics that are commonly considered in the models. We conclude that there are fundamental theoretical differences among the mechanisms of emotion contagion that reflect a difference in view on the contagion process and its application, although findings from comparative studies are inconclusive. Further, while large theoretical progress has been made in recent years, empirical evaluation of the proposed models is lagging behind due to the complexity of reliably measuring emotions and context in large groups. We make several suggestions on a way forward regarding validation to eventually justify the application of models of emotion contagion in society.

IJCAI Conference 2022 Conference Paper

Automatic Recognition of Emotional Subgroups in Images

• Emmeke Veltmeijer
• Charlotte Gerritsen
• Koen Hindriks

Both social group detection and group emotion recognition in images are growing fields of interest, but never before have they been combined. In this work we aim to detect emotional subgroups in images, which can be of great importance for crowd surveillance or event analysis. To this end, human annotators are instructed to label a set of 171 images, and their recognition strategies are analysed. Three main strategies for labeling images are identified, with each strategy assigning either 1) more weight to emotions (emotion-based fusion), 2) more weight to spatial structures (group-based fusion), or 3) equal weight to both (summation strategy). Based on these strategies, algorithms are developed to automatically recognize emotional subgroups. In particular, K-means and hierarchical clustering are used with location and emotion features derived from a fine-tuned VGG network. Additionally, we experiment with face size and gaze direction as extra input features. The best performance comes from hierarchical clustering with emotion, location and gaze direction as input.

AAMAS Conference 2021 Conference Paper

Agent Programming in the Cognitive Era

• Rafael H. Bordini
• Amal El Fallah Seghrouchni
• Koen Hindriks
• Brian Logan
• Alessandro Ricci

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers [10]. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments [14]. In this paper, we argue that the unique strengths of Belief-Desire-Intention (BDI) agent programming languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems.

AAAI Conference 2017 Conference Paper

Automated Negotiating Agents Competition (ANAC)

• Catholijn Jonker
• Reyhan Aydogan
• Tim Baarslag
• Katsuhide Fujita
• Takayuki Ito
• Koen Hindriks

The annual International Automated Negotiating Agents Competition (ANAC) is used by the automated negotiation research community to benchmark and evaluate its work andto challenge itself. The benchmark problems and evaluation results and the protocols and strategies developed are available to the wider research community.

AIJ Journal 2013 Journal Article

Evaluating practical negotiating agents: Results and analysis of the 2011 international competition

• Tim Baarslag
• Katsuhide Fujita
• Enrico H. Gerding
• Koen Hindriks
• Takayuki Ito
• Nicholas R. Jennings
• Catholijn Jonker
• Sarit Kraus

AAAI Conference 2013 Conference Paper

Multi-Cycle Query Caching in Agent Programming

• Natasha Alechina
• Tristan Behrens
• Mehdi Dastani
• Koen Hindriks
• Jomi Hubner
• Brian Logan
• Hai Nguyen
• Marc van Zee

In many logic-based BDI agent programming languages, plan selection involves inferencing over some underlying knowledge representation. While context-sensitive plan selection facilitates the development of flexible, declarative programs, the overhead of evaluating repeated queries to the agent’s beliefs and goals can result in poor run time performance. In this paper we present an approach to multi-cycle query caching for logic-based BDI agent programming languages. We extend the abstract performance model presented in (Alechina et al. 2012) to quantify the costs and benefits of caching query results over multiple deliberation cycles. We also present results of experiments with prototype implementations of both single- and multi-cycle caching in three logic-based BDI agent platforms, which demonstrate that significant performance improvements are achievable in practice.

AAMAS Conference 2010 Conference Paper

Formalizing Organizational Constraints: A Semantic Approach

• M. Birna van Riemsdijk
• Koen Hindriks
• Catholijn Jonker
• Maarten Sierhuis

An organizational modeling language can be used to specify anagent organization in terms of its roles, organizational structure, norms, etc. Such an organizational specification imposes constraintson agents that play roles in it, and the agents are expected to takethis into account when deciding what to do. This means that agentsneed to have a basic understanding of what it means to complywith organizational constraints. For this, it is essential that theseconstraints are precisely specified. In this paper, we address thisin the context of the MOISE+ organizational modeling language. We define a semantic framework for MOISE+ MAS and an accompanying linear temporal logic (LTL) to express its properties. Weanalyze which constraints MOISE+ imposes on agents, and investigate how these can be made precise in LTL. We show that multipleinterpretations of constraints are sometimes possible, and explorethe space of possibilities. These analyses demonstrate the need fora rigorous specification of organizational constraints, and providethe foundations for the development of agents that understand howto function in a MOISE+ MAS.

AAMAS Conference 2008 Conference Paper

Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning

• Dmytro Tykhonov
• Koen Hindriks

The efficiency of automated multi-issue negotiation depends on the availability and quality of knowledge about an opponent. We present a generic framework based on Bayesian learning to learn an opponent model, i. e. the issue preferences as well as the issue priorities of an opponent. The algorithm proposed is able to effectively learn opponent preferences from bid exchanges by making some assumptions about the preference structure and rationality of the bidding process. The assumptions used are general and consist among others of assumptions about the independency of issue preferences and the topology of functions that are used to model such preferences. Additionally, a rationality assumption is introduced that assumes that agents use a concession-based strategy. It thus extends and generalizes previous work on learning in negotiation by introducing a technique to learn an opponent model for multi-issue negotiations. We present experimental results demonstrating the effectiveness of our approach and discuss an approximation algorithm to ensure scalability of the learning algorithm.