Within many domains, among which biological and cognitive areas, multiple interacting processes occur among agents with dynamics that are hard to handle. Current approaches to analyse the dynamics of such processes, often based on differential equations, are not always successful. As an alternative to differential equations, this paper presents the predicate logical Temporal Trace Language (TTL) for the formal specification and analysis of dynamic properties. This language supports the specification of both qualitative and quantitative aspects, and therefore subsumes specification languages based on differential equations. A software environment has been developed for TTL, that supports editing TTL properties and enables the formal verification of properties against a set of traces. The TTL environment proved its value in a number of projects within different domains. 1.

Within many domains, among which biological, cognitive, and social areas, multiple interacting processes occur among agents with dynamics that are hard to handle. This paper presents the predicate logical Temporal Trace Language (TTL) for the formal specification and analysis of dynamic properties of agents and multi-agent systems. This language supports the specification of both qualitative and quantitative aspects, and therefore subsumes specification languages based on differential equations and qualitative, logical approaches. A software environment has been developed for TTL, which supports editing TTL properties and enables the formal verification of properties against a set of traces. The TTL environment proved its value in a number of projects within different biological, cognitive and social domains.

This paper contributes an analysis and formalisation of Damasio’s theory on core consciousness. Three important concepts in this theory are ‘emotion’, ‘feeling’, and ‘feeling a feeling ’ (or core consciousness). In particular, a simulation model is described of the dynamics of basic mechanisms leading via emotion and feeling to core consciousness, and dynamic properties are formally specified that hold for these dynamics at a more global level. These properties have been automatically checked for the simulation model. Moreover, a formal analysis is made of relevant notions of representation used by Damasio. As part of this analysis, specifications of representation relations have been verified and confirmed against the simulation model. 1

This paper introduces a formal BDI-based agent model for the concept of Theory of Mind. The model uses BDI-concepts to describe the reasoning process of an agent that reasons about the reasoning process of another agent, which is also based on BDIconcepts. A case study illustrates how the model can be used for social manipulation. This case study addresses the scenario of a manager that reasons about the task avoiding behaviour of his employee. For this scenario, a number of simulation experiments have been performed, and some of their results are discussed. 1.

In this paper a simulation model for visual attention is discussed and formally analysed. The model is part of the design of an agent-based system that supports a naval officer in its task to compile a tactical picture of the situation in the field. A case study is described in which the model is used to simulate a human subject’s attention. The formal analysis is based on temporal relational specifications for attentional states and for different stages of attentional processes. The model has been automatically verified against these specifications.

This paper introduces a computational model for emotion regulation formalising the model informally described by Gross (1998). The model has been constructed using a highlevel modelling language, and integrates both quantitative aspects (such as levels of emotional response) and qualitative aspects (such as decisions to regulate one’s emotion). A number of simulation experiments have been performed, demonstrating that the computational model successfully reflects the model as described by Gross.

Abstract. A new, formal, role-based, framework for modeling and analyzing both real world and artificial organizations is introduced. It exploits static and dynamic properties of the organizational model and includes the (frequently ignored) environment. The transition is described from a generic framework of an organization to its deployed model and to the actual agent allocation. For verification and validation purposes, a set of dedicated techniques is introduced. Moreover, where most models can handle only two or three layered organizational structures, our framework can handle any arbitrary number of organizational layers. Henceforth, real-world organizations can be modeled and analyzed, as illustrated by a case study, within the DEAL project line. 1

This paper focuses on how capabilities to interpret another agent’s emotions, and their biological realisation can be modelled. First a cognitive and a biological agent model to generate emotional states are introduced. These models involve the generation and sensing of body states. Next it is shown how emotion reading can be modelled both at a cognitive and at a biological level, following the Simulation Theory approach to mindreading. Furthermore, a cognitive Theory Theory model for emotion reading is presented. Finally, it is shown how the cognitive and biological agent models can be related. 1.

In this paper a cognitive model for visual attention is introduced. The cognitive model is part of the design of a software agent that supports a naval warfare officer in its task to compile a tactical picture of the situation in the field. An executable formal specification of the cognitive model is given and a case study is described in which the model is used to simulate a human subject’s attention. The foundation of the model is based on formal specification of representation relations for attentional states, specifying their intended meaning. The model has been automatically verified against these relations. 1.

Abstract. Agent-based simulation methods are a relatively new way to address complex systems. Usually the idea is that the agents used are rather simple, and the complexity and adaptivity of such a system are modelled by the interaction between these agents. However, another way to exploit agent-based simulation methods is by use of agents that themselves also have certain forms of learning or adaptation. In order to simulate adaptive agents with abilities matching those of their real-world biological or societal counterparts, a natural approach is to incorporate certain adaptation mechanisms such as classical conditioning into agent models. Existing models for adaptation mechanisms are usually based on quantitative, numerical methods, and more in particular, differential equations. Since agent-based simulation is usually based on qualitative, logical languages, these quantitative models are often not directly appropriate as an input in the context of agent-based simulation. To deal with this problem, this paper puts forward an integrative approach to simulate and analyse the dynamics of a conditioning process of an adaptive agent, integrating quantitative, numerical and qualitative, logical aspects within one expressive temporal specification language. To obtain a simulation model, an executable sublanguage of this language is used to specify the agent’s adaptation