To Örebro University

In this paper we propose an ontology pattern that represents paths in a geo-representation model to be used in an aerial path planning processes. This pattern provides semantics related to constraints (i.e., ight forbidden zones) in a path planning problem in order to generate collision free paths. Our proposed approach has been applied on an ontology containing geo-regions extracted from satellite imagery data from a large urban city as an illustrative example.

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

This paper depicts a sensor-based map navigation approach which targets users, who due to disabilities or lack of technical knowledge are currently not in the focus of map system developments for personalized information. What differentiates our approach from the state-of-art mostly integrating localized social media data, is that our vision is to integrate real time sensor generated data that indicates the situation of dfferent phenomena (such as the physiological functions of the body) related to the user. The challenge hereby is mainly related to knowledge representation and integration. The tentative impact of our vision for future navigation systems is re ected within a scenario.

One way to cope with the increasing traffic demand is to integrate standard solutions with more intelligent control measures. However, the result of possible interferences between intelligent control or information provision tools and other components of the overall traffic system is not easily predictable. This paper discusses the effects of integrating co-adaptive decision-making regarding route choices (by drivers) and control measures (by traffic lights). The motivation behind this is that optimization of traffic light control is starting to be integrated with navigation support for drivers. We use microscopic, agent-based modelling and simulation, in opposition to the classical network analysis, as this work focuses on the effect of local adaptation. In a scenario that exhibits features comparable to real-world networks, we evaluate different types of adaptation by drivers and by traffic lights, based on local perceptions. In order to compare the performance, we have also used a global level optimization method based on genetic algorithms.

In the last few years, the number of papers devoted to applications of agent-based technologies to traffic and transportation engineering has grown enormously. Thus, it seems to be the appropriate time to shed light over the achievements of the last decade, on the questions that have been successfully addressed, as well as on remaining challenging issues. In the present paper, we review the literature related to the areas of agent-based traffic modelling and simulation, and agent-based traffic control and management. Later we discuss and summarize the main achievements and the challenges.

Travel apps become more and more popular giving in-formation about the current traffic state to drivers who then adapt their route choice. In commuting scenarios, where people repeatedly travel between a particular origin and destination, learning effects add to this information. In this paper, we analyse the effects on the overall network, if adaptive driver agents share their aggregated experience about route choice in a reinforcement learning (Q-learning) setup. Drivers share what they have learnt about the system, not just information about their current travel times. We can show in a standard scenario that experience sharing can improve convergence times for adaptive driver agents

Human drivers may perform replanning when facing traffic jams or when informed that there are expected delays on their planned routes. In this paper, we address the effects of drivers re-routing, an issue that has been ignored so far. We tackle re-routing scenarios, also considering traffic lights that are adaptive, in order to test whether such a form of co-adaptation may result in interferences or positive cumulative effects. An abstract route choice scenario is used which resembles many features of real world networks. Results of our experiments show that re-routing indeed pays off from a global perspective as the overall load of the network is balanced. Besides, re-routing is useful to compensate an eventual lack of adaptivity regarding traffic management.

Urban mobility is not only one of the pillars of modern economic systems, but also a key issue in the quest for equality of opportunity, once it can improve access to other services. Currently, however, there are a number of negative issues related to traffic, especially in mega-cities, such as economical issues (cost of opportunity caused by delays), environmental (externalities related to emissions of pollutants), and social (traffic accidents). Solutions to these issues are more and more closely tied to information and communication technology. Indeed, a search in the technical literature (using the keyword ``urban traffic" to filter out articles on data network traffic) retrieved the following number of articles (as of December 3, 2013): 9,443  (ACM Digital Library), 26,054 (Scopus), and 1,730,000 (Google Scholar). Moreover, articles listed in the ACM query relate to conferences as diverse as MobiCom, CHI, PADS, and AAMAS. This means that  there is a big and diverse community of computer scientists and computer engineers who tackle research that is connected to the development of intelligent traffic and transportation systems. It is also possible to see that this community is growing, and that research projects are getting more and more interdisciplinary. To foster the cooperation among the involved communities, this book aims at  giving a broad introduction into the basic but relevant concepts related to transportation systems, targeting researchers and practitioners from computer science and information technology. In addition, the second part of the book gives a panorama of some of the most exciting and newest technologies, originating in computer science and computer engineering, that are now being employed in projects related to car-to-car communication, interconnected vehicles, car navigation, platooning, crowd sensing and sensor networks, among others. This material will also be of interest to engineers and researchers from the traffic and transportation community.

Q-Networks are used in Reinforcement Learning to model the expected return from every action at a given state. When training Q-Networks, external reward signals are propagated to the previously performed actions leading up to each reward. If many actions are required before experiencing a reward, the reward signal is distributed across all those actions, where some actions may have greater impact on the reward than others. As the number of significant actions between rewards increases, the relative importance of each action decreases. If actions have too small importance, their impact might be over-shadowed by noise in a deep neural network model, potentially causing convergence issues. In this work, we empirically test the limits of increasing the number of actions leading up to a reward in a simple grid-world environment. We show in our experiments that even though the training error surpasses the reward signal attributed to each action, the model is still able to learn a smooth enough value representation.

The environment is a key point when talking about MASapplications, being a key concept when developing a platform or appli-cation in the past ten years: what is important in it and how to access it.At the same time, technology has evolved so that Virtual Environment-kinds of applications have grown out of science ction novels till researchpapers and even real applications. Current technology makes possible toMAS to interact also in this environments.In this paper, we have looked for the common ground that have all thedierent domains relating Virtual Environments as E4MAS, and we havecharacterized those domains according to three dimensions: connectionto the physical world of the environment, agents nature, and sociability.Moreover, we comment one of these domains, Mirror Worlds, as it is oneof the most complex domains commented, that we believe that is one ofthe topics to take into account in the near future both as a researh anddeveloping domain.

Since more than a decade, the environment is seen as a key element when analyzing, developing or deploying Multi-Agent Systems (MAS) applications. Especially, for the development of multi-agent platforms, it has become a key concept, similarly to many application in the area of location-based, distributed systems. An emerging, prominent application area for MAS is related to Virtual Environments. The underlying technology has evolved in a way, that these applications have grown out of science fiction novels till research papers and even real applications. Even more, current technologies enable MAS to be key components of such virtual environments.

In this paper, we widen the concept of the environment of a MAS to encompass new and mixed physical, virtual, simulated, etc. forms of environments. We analyze currently most interesting application domains based on three dimensions: the way different "realities" are mixed via the environment, the underlying natures of agents, the possible forms and sophistication of interactions. In addition to this characterization, we discuss how this widened concept of possible environments influences the support it can give for developing applications in the respective domains.

Understanding and managing complex systems has become one of the biggest challenges for research, policy and industry. Modeling and simulationof complex systems promises to enable us to understand how a human nervous systemand brain not just maintain the activities of a metabolism, but enable the production of intelligent behavior, how huge ecosystems adapt to changes, or what actually influences climatic changes. Also man-made systems are getting more complex and difficult, or even impossible, to grasp. Therefore we need methods and tools that can help us in, for example, estimating how different infrastructure investments will affect the transport system and understanding the behavior of large Internet-based systems in different situations. This type of system is becoming the focus of research and sustainable management as there are now techniques, tools and the computational resources available. This chapter discusses modeling and simulation of such complex systems. We will start by discussing what characterizes complex systems.

Due to the "generative" nature of the macro phenomena, agent-based systems require experience from the modeler to determine the proper low-level agent behavior. Adaptive and learning agents can facilitate this task: Partial or preliminary learnt versions of the behavior can serve as inspiration for the human modeler. Using a simulation process we develop agents that explore sensors and actuators inside a given environment. The exploration is guided by the attribution of rewards to their actions, expressed in an objective function. These rewards are used to develop a situation-action mapping, later abstracted to a human-readable format. In this contribution we test the robustness of a decision-tree-representation of the agent's decision-making process with regards to changes in the objective function. The importance of this study lies on understanding how sensitive the definition of the objective function is to the final abstraction of the model, not merely to a performance evaluation.

The process of finding the appropriate agent behavior is a cumbersome task – no matter whether it is for agent-based software or simulation models. Machine Learning can help by generating partial or preliminary versions of the agent low-level behavior. However, for actually being useful for the human modeler the results should be interpretable, which may require some post-processing step after the actual behavior learning. In this contribution we test the sensitivity of the resulting, interpretable behavior program with respect to parameters and components of the function that describes the intended behavior.

There have been a number of suggestions for methodologies supporting the development of multiagent simulation models. In this contribution we are introducing a learning-driven methodology that exploits learning techniques for generating suggestions for agent behavior models based on a given environmental model. The output must be human-interpretable. We compare different candidates for learning techniques - classier systems, neural networks and reinforcement learning - concerning their appropriateness for such a modeling methodology.

Developing a valid agent-based simulation model is not always straight forward, but involves a lot of prototyping, testing and analyzing until the right low-level behavior is fully specified and calibrated. Our aim is to replace the try and error search of a modeler by adaptive agents which learn a behavior that then can serve as a source of inspiration for the modeler. In this contribution, we suggest to use genetic programming as the learning mechanism. For this aim we developed a genetic programming framework integrated into the visual agent-based modeling and simulation tool SeSAm, providing similar easy-to-use functionality.

One major challenge in developing multiagent systems is to find the appropriate agent design that is able to generate the intended overall dynamics, but does not contain unnecessary features. In this article we suggest to use agent learning for supporting the development of an agent model during an analysis phase in agent-based software engineering. Hereby, the designer defines the environmental model and the agent interfaces. A reward function captures a description of the overall agent performance with respect to the intended outcome of the agent behavior. Based on this setup, reinforcement learning techniques can be used for learning rules that are optimally governing the agent behavior. However, for really being useful for analysis, the human developer must be able to review and fully understand the learnt behavior program. We propose to use additional learning mechanisms for a post-processing step supporting the usage of the learnt model.

One major challenge in developing multiagent simulations is to find the appropriate agent design that is able to generate the intended overall phenomenon dynamics, but does not contain unnecessary details. In this paper we suggest to use agent learning for supporting the development of an agent model: the modeler defines the environmental model and the agent interfaces. Using rewards capturing the intended agent behavior, reinforcement learning techniques can be used for learning the rules that are optimally governing the agent behavior. However, for really being useful in a modeling and simulation context, a human modeler must be able to review and understand the outcome of the learning. We propose to use additional forms of learning as post-processing step for supporting the analysis of the learned model. We test our ideas using a simple evacuation simulation scenario.

The question of what is the best way to develop an agent-based simulation model becomes more important as this paradigm is more and more used. Clearly, general model development processes can be used, but these do not solve the major problems of actually deciding about the agents' structure and behavior. In this contribution we introduce the MABLe methodology for analyzing and designing agent simulation models that relies on adaptive agents, where the agent helps the modeler by proposing a suitable behavior program. We test our methodology in a pedestrian evacuation scenario. Results demonstrate the agents can learn and report back to the modeler a behavior that is interestingly better than a hand-made model.

Designing the agent model in a multiagent simulation is a challenging task due to the generative nature of such systems. In this contribution we present an extension to the multiagent simulation platform SeSAm, introducing a learning-based design strategy for building agent behavior models.

Designing a proper agent behavior for a multiagent simulation is a complex task as it is not obvious how the agents actions, and interactions among them and with their environment, result in an intended macro-phenomenon. To cope with the complexity involved in this challenge, and to achieve the intended overall result, the modeler may benefit from using agent learning techniques. In this contribution we focus on testing different configurations of the interface between the learning algorithm and the simulation scenario. The learned result is post-processed by a decision tree learner, to derive a comprehensible model for the agent behavior.

In this project report, we describe ongoing research on supporting the development of agent-based simulation models. The vision is that the agents themselves should learn their (individual) behavior model, instead of letting a human modeler test which of the many possible agent-level behaviors leads to the correct macro-level observations. To that aim, we integrate a suite of agent learning tools into SeSAm, a fully visual platform for agent-based simulation models. This integration is the focus of this contribution.

The process of creation and validation of an agentbased simulation model requires the modeler to undergo a number of prototyping, testing, analyzing and re-designing rounds. The aim is to specify and calibrate the proper low level agent behavior that truly produces the intended macro level phenomena. We assume that this development can be supported by agent learning techniques, specially by generating inspiration about behaviors as starting points for the modeler. In this contribution we address this learning-driven modeling task and compare two methods that are producing decision trees: reinforcement learning with a post-processing step for generalization and Genetic Programming.

Designing the proper agent behavior for a multiagent system is a complex task. Often it is not obvious which of the agents' actions, and the interactions among them and with their environment, can produce the intended macro-phenomenon. We assume that the modeler can benefit from using agent-learning techniques. There are several issues with which learning can help modeling; for example, by using self-adaptive agents for calibration. In this contribution we are dealing with another example: the usage of learning for supporting system analysis and model design. A candidate-learning architecture is the combination of reinforcement learning and decision tree learning. The former generates a policy for agent behavior and the latter is used for abstraction and interpretation purposes. Here, we focus on the relation between policy-learning convergence and the quality of the abstracted model produced from that.

Validity forms the basic prerequisite for every simulation model, therefore also for reasonable usage of the agent-based simulation paradigm. However, models based on the multi-agent system metaphor tend to need some particular approaches. In this paper, I propose a process for validating agent-based simulation models that combines face validation, sensitivity analysis, calibration and statistical validation.

When designing and implementing an Agent-Based Simulation model a major challenge is to formulate the interactions between agents and between agents and their environment. In this contribution we present an approach for capturing agent-environment interactions based on the “affordance” concept. Originated in ecological psychology, affordances represent relations between environmental objects and potential actions that an agent may perform with those objects and thus offer a higher abstraction level for dealing with potential interaction. Our approach has two elements: a methodology for using the affordance concept to identify interactions and secondly, a suggestion for integrating affordances into agents’ decision making. We illustrate our approach indicating an agent-based model of after-earthquake behavior.

The history of agent-based models started in the 1970ies with singular yet path-breaking examples such as the Segregation model by T. Schelling [Schelling, 1971]. From end of the 80ies on more and more agent-based models were developed and implemented. However, almost no simulation engineering happened. Due to the relation to social sciences, mostly sociologists and psychologists used the paradigm of simulated humans based on rather complex models of human decision making to model hypotheses and theories about societal dynamics. The resulting models were complex but abstract. The role of empirical embeddedness is still discussed in the area of social simulation. Practioneers from more engineering-oriented domains like traffic simulation or researchers from domains with long simulation background like theoretical biology or engineering found the techniques associated with agent-based simulation interesting, yet not mature enoughto actually apply them.

Agent-based simulation definitely is a highly valuable tool, especially when studying complex self-organizing systems in many domains. Thus, the question arises, what shows the maturity of a simulation paradigm and how the achievement of a high level of applicability can be brought forward? The answer is basically that engineering-like development and some form of good practice have to be established. In particular, this leads to the following issues that have to be addressed for fostering the development of agent-based models.

• Deep understanding of the “object”, that means understanding of agent-based models themselvesand what particular feature is useful in what particular context.
• Development of best practice: Establishing knowledge about how to build an agent-basedmodel efficiently and in a way that costs can be a priori estimated.

Until now, none of these items is solved in a satisfying way. However, they are necessarily achieved at least partially for improving the broad applicability of agent-based modeling and simulation. Steps leading to the general aim of this book – fostering the applicability of agent-based simulation – can be derived from these considerations.

A basic prerequisite and therefore first step is collecting specific knowledge about agent-based simulation and the context of its appropriate application. This refers to properties of simulation questions and modeling targets as well as to theoretical and empirical requirements for model design, implementation and usage.

The second step concerns the development of an agent-based simulation. Although the general process model for developing simulation models, presented in every simulation textbook, can also be applied for agent-based simulation, the problem goes deeper than just using an appropriate specification or implementation language. Agent-based simulations are generative. It is not jus tdescribing what was observed, but finding agent behavior and interactions that produce a particular phenomenon. This idea has several consequences ranging from missing micro-macro links over non-linear models and tendencies to full detail to several levels of validation. Thus, developing methods for bridging the gap between macro-level objectives and appropriate micro-level programs in a systematic and reproducible way is the challenge for agent-based simulation engineering.

A third step must consider practical application of the theoretical foundations. Basically,learning how to model for simulation possesses the same characteristics as learning how to program software. One might read about language constructs, but how its actually working is only experience-able by doing it. Therefore, a detailed presentation of simulation models and theirconstruction has to be part of a book about simulation engineering.

Thus, this book sums up experiences in methodological research and application of agent-based simulation, especially in modeling complex and self-organizing systems. This book is a further step towards systematic engineering of agent-based models involving appropriate meta-models, procedures for development, conceptual and technical design and validation of models. It bridges the gap between established techniques related to modeling and simulation and the approaches and requirements for complex agent-based simulation modeling.

Multiagent simulation emerges to be one of the "killer applications" of multiagent system technology. For several reasons, there is a serious lack of engineering approaches in developing simulation models, so connecting AOSE with Multiagent Simulation seems to end in a win-win situation. A basic prerequisite is hereby to understand the current state and challenges of developing multiagent simulations. This is the objective of this contribution.

When designing an Agent-Based Simulation Model a central challenge is to formulate the appropriate interactions between agents as well as between agents and their environment. In this contribution we present the idea of capturing agent-environment interactions based on the “affordance” concept. Originating in ecological psychology, affordances represent relations between environmental objects and potential actions that agents may perform using those objects. We assume that explicitly handling affordances based on semantic annotation of entities in simulated space may offer a higher abstraction level for dealing with potential interaction. Our approach has two elements: firstly a methodology for using the affordance concept to identify interactions and secondly a suggestion for integrating affordances into agents’ decision making. We illustrate our approach indicating an agent-based model of after-earthquake behavior.

This article gives an introduction to agent-based modeling and simulation (ABMS). After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom-up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application-oriented aspects, including selected tools and well-known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well-known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challenges is given.

Navigation apps have become more and more popular, as they give information about the current traffic state to drivers who then adapt their route choice. In commuting scenarios, where people repeatedly travel between a particular origin and destination, people tend to learn and adapt to different situations. What if the experience gained from such a learning task is shared via an app? In this paper, we analyse the effects that adaptive driver agents cause on the overall network, when those agents share their aggregated experience about route choice in a reinforcement learning setup. In particular, in this investigation, Q-learning is used and drivers share what they have learnt about the system, not just information about their current travel times. Using a classical commuting scenario, we show that experience sharing can improve convergence times that underlie a typical learning task. Further, we analyse individual learning dynamics to get an impression how aggregate and individual dynamics are related to each other. Based on that interesting pattern of individual learning dynamics can be observed that would otherwise be hidden in an only aggregate analysis. 

In this contribution, we propose an adaptation-drivenmethodology for the technical design and implementation of multi-agent simulations that is inspired by the concept of "living design". The simulated agents are capable of evaluatingtheir behavior and self-adapt for improving the overall model.For this aim, the modeler describes critical, non valid situations in the life of an agent, or the complete agent system, and explicitly specifies repair knowledge for these situations.

The basic prerequisite for methodological advance in Multi-Agent Based Modelling and Simulation is a clear, ideally formally-grounded, concept of our subject. A commonly accepted, implementation-independent meta-model may improve the status of MABS as a scientific field providing a solid foundation that can be used for describing, comparing, analysing, and understanding MABS models. In this contribution, we present an attempt formalizing a general view of MABS models by defining the AMASON meta-model that captures the basic structure and dynamics of a MABS model.

Although multi-agent simulations are an intuitive way of conceptualizing systems that consist of autonomous actors, a major problem is the actual design of the agent behavior. In this contribution, we examine the potential of using agent-based learning for implementing the agent behavior. We enhanced SeSAm, a platform for agent-based simulation, by replacing the usual rule-based agent architecture by XCS, a well-known learning classifier system (LCS). The resulting model is tested using a simple evacuation scenario. The results show that on the one hand side plausible agent behavior could be learned. On the other hand side, though, the results are quite brittle concerning the frame of environmental feedback, perception and action modeling.

The formalization and use of experiences in good model design would make an important contribution to increasing the efficiency of modeling as well as to supporting the knowledge transfer from experienced modelers to modeling novices. We propose to address this problem by providing a set of model design patterns inspired by patterns in Software Engineering for capturing the reusable essence of a solution to specific partial modeling problem. This contribution provides a First step formulating the vision and indicating how patterns and which types of patterns can play a role in agent-based model design.

Simulating evacuation processes forms an established way of layout evaluation or testing routing strategies or sign location. A variety of simulation projects using different microscopic modeling and simulation paradigms has been performed. In this contribution, we are presenting a particular simulation project that evaluates different emergency system layout for a planned train tunnel. The particular interesting aspect of this project is the integration of realistic dynamic environmental data about temperature and smoke propagation and its effect on the agents equipped with high-level abilities.

This paper studies the role of trade for the joint uptake of AI-enabled automation in manufacturing and engineering. It develops an agent-based model (ABM) where the agents are heterogeneous manufacturers and engineering firms. The ABM features two technology-related business models: engineering as a face-to-face consultancy service and engineering as automated software. The software adoption rate follows an S-shaped curve for manufacturers and a boom and bust cycle for engineers. In the early phase, shortage of engineers constrains AI uptake, while engineers become abundant when AI is fully adopted. Trade affects the cut-off productivity level at which manufacturers switch technology, the shape of the adoption rate curve, and the incentives for engineers to develop software. Bulky transactions and different productivity distributions across countries are drivers of trade in their own right.

Agent-based Simulation Modelling focuses on the agents' decision making in their individual context. The decision making details may substantially affect the simulation outcome, and therefore need to be carefully designed.

In this paper we contrast two decision making architectures: a process oriented approach in which agents generate expectations and a reinforcement-learning based architecture inspired by evolutionary game theory. We exemplify those architectures using a technology uptake model in which agents decide about adopting automation software. We find that the end result is the same with both decision making processes, but the path towards full adoption of software differs. Both sets of simulations are robust, explainable and credible. The paper ends with a discussion what is actually gained from replacing behaviour description by learning.

This book constitutes the proceedings of the 9th German Conference on Multiagent System Technologies held in Berlin, Germany, in October 2011. The 12 revised full papers presented together with 6 short parers were carefully reviewed and selected from 50 submissions. Providing an interdisciplinary forum for researchers, users, and developers to present and discuss latest advances in research work as well as prototyped or fielded systems of intelligent agents and multi-agent systems, the papers cover the whole range of this sector and promote its theory and applications.

Mode and route choice are central elements of traffic simulations. Traditionally they form two subsequent steps in the four-step process where first, the simulated population distributes among available transportation modes and then their movement is assigned to the roads respectively other networks. However, these two phases are better dealt with simultaneously as choices are highly depending on each other. In this paper, we are suggesting an agent-based combined route and mode choice model that is not only able to resemble traditional simulations, but provides the means for new applications. As the simulated agents are active and situated while moving through the network, they are able to react to unforeseen events such as the closing of a link. Thus we can reproduce the self-organized re-distribution of travelers to new routes depending on when/where they are notified about the problem. We illustrate the feasibility and usefulness of our agent-based mode and route choice simulation using a real-world network of a small-size Swiss town.

Over the last years, the affordance concept has attracted more and more attention in agent-based simulation. Due to its grounding in cognitive science, we assume that it may help a modeller to capture possible interactions in the modelling phase as it can be used to clearly state under which circumstances an agent might execute a particular action with a particular environmental entity.

In this discussion paper we clarify the concept of affordance and introduce a light-weight formalization of the notions in a way appropriate for agent-based simulation modelling. We debate its suitability for capturing interaction compared to other approaches.

Over the last years, the affordance concept has attracted more and more attention in agent-based simulation. Due to its grounding in cognitive science, we assume that it may help a modeller to capture possible interactions in the modelling phase as it can be used to clearly state under which circumstances an agent might execute a particular action with a particular environmental entity.

In this discussion paper we clarify the concept of affordance and introduce a light-weight formalization of the notions in a way appropriate for agent-based simulation modelling. We debate its suitability for capturing interaction compared to other approaches.