From Sensing to Action: Quick and Reliable Access to Information in Cities Vulnerable to Heavy Rain

Cities need to constantly monitor weather to anticipate heavy storm events and reduce the impact of floods. Information describing precipitation and ground conditions at high spatio-temporal resolution is essential for taking timely action and preventing damages. Traditionally, rain gauges and weather radars are used to monitor rain events, but these sources provide low spatial resolutions and are subject to inaccuracy. Therefore, information needs to be complemented with data from other sources: from citizens' phone calls to the authorities, to relevant online media posts, which have the potential of providing timely and valuable information on weather conditions in the city. This information is often scattered through different, static, and not-publicly available databases. This makes it impossible to use it in an aggregate, standard way, and therefore hampers efficiency of emergency response. In this paper, we describe information sources relating to a heavy rain event in Rotterdam on October 12-14, 2013. Rotterdam weather monitoring infrastructure is composed of a number of rain gauges installed at different locations in the city, as well as a weather radar network. This sensing network is currently scarcely integrated and logged data are not easily accessible during an emergency. Therefore, we propose a reliable, efficient, and low-cost ICT infrastructure that takes information from all relevant sources, including sensors as well as social and user contributed information and integrates them into a unique, cloud-based interface. The proposed infrastructure will improve efficiency in emergency responses to extreme weather events and, ultimately, guarantee more safety to the urban population

Externalisation and Internalization: A New Perspective on Agent Modularisation in Multi-Agent System Programming

Abstract—Agent modularisation is a main issue in agent and multi-agent system programming. Existing solutions typically propose some kinds of constructs – such as capabilities – to group and encapsulate in well-defined modules inside the agent different kinds of agent features, that depend on the architecture or model adopted—examples are goals, beliefs, intentions, skills. In this paper we introduce a further perspective, which can be considered complimentary to existing approaches, which accounts for externalizing some of such functionalities into the computational environment where agents are (logically) situated. In this perspective, agent modules are realised as suitably designed artifacts that agents can dynamically exploit as external tools to enhance their action repertoire and – more generally – their capability to execute tasks. Then, to let agent (and agent programmers) exploit such capabilities abstracting from the low-level mechanics of artifact management and use, we exploit the dual notion of internalization, which consists in dynamically consulting and automatically embedding high-level usage protocols described in artifact manuals as agent plans. The idea is discussed providing some practical examples of use, based on CArtAgO as technology for programming artifacts and Jason agent platform to program the agents. I

Cognitive agent programming : A semantic approach

In this thesis we are concerned with the design and investigation of dedicated programming languages for programming agents. We focus in particular on programming languages for rational agents, i.e., flexibly behaving computing entities that are able to make "good" decisions about what to do. An important line of research in this area is based on Bratman’s so-called Belief Desire Intention (BDI) philosophy. The idea of BDI philosophy is that the behavior of rational agents can be predicted by ascribing beliefs, desires, and intentions to the agent, and by assuming that the agent will tend to act in pursuit of its desires, taking into account its beliefs about the world. The idea was then coined that it might not only be possible to explain and describe rational agents in terms of the BDI notions, but that it might also be possible to program rational agents, using these notions as first class citizens in a programming language. The research that is done along these lines not only uses the notions of beliefs, desires, and intentions, but also related notions such as goals and plans. We refer to these notions as "cognitive" notions, and to programming languages for agents based on these notions as "cognitive agent programming languages". Our work proposes new constructs for representing these cognitive notions in a programming language, and investigates existing constructs. We take a semantic approach, in that we define formal semantics for the proposed constructs, and investigate the constructs by performing a semantic analysis. We investigate in particular ways for representing goals, and we study a construct called "plan revision rule" of the cognitive agent programming language 3APL, which can be used for revising an agent’s plan if the circumstances call for this. Regarding the representation of goals, we investigate the representation of subgoals in the plans of agents. We show how declarative subgoals, i.e., subgoals representing a desired state, can be programmed in 3APL, even though the semantics of subgoals of 3APL defines them to behave in procedurally. Further, we propose a semantics for the representation of conflicting goals that is based on default logic, and investigate properties of this semantics. Also, we provide an analysis of ways in which goals have been represented in cognitive agent programming languages. Regarding plan revision rules, we analyze the semantic issues that arise with the introduction of these rules. That is, the semantics of plan execution becomes non-compositional with the introduction of these rules. This is problematic when reasoning about the execution of plans. We propose a dynamic logic that is tailored to handle plan revision by circumventing the non-compositionality issue in a certain way, and we show how these rules can be restricted such that the semantics becomes compositional again. Finally, we propose a way to introduce support for modularization in cognitive agent programming languages that is based on the goals of the agent, and we show that the Maude term rewriting language is well suited for implementing logic-based cognitive agent programming languages

High resolution weather data for urban hydrological modelling and impact assessment, ICT requirements and future challenges

Water ManagementCivil Engineering and Geoscience

State Space Reduction for Model Checking Agent Programs

State space reduction techniques have been developed to increase the efficiency of model checking in the context of imperative programming languages. Unfortunately, these techniques cannot straightforwardly be applied to agents: the nature of states in the two programming paradigms differs too much for this to be possible. To resolve this, we adapt core definitions on which existing reduction algorithms are based to agents. Moreover, the framework that we introduce is such that different reduction algorithms can be defined in terms of the same relations. This is beneficial because it enables the reuse of code and reduces computation time when different techniques are used simultaneously. Specifically, we adapt and combine two known techniques: property-based slicing and partial order reduction. We exemplify our work with the GOAL agent programming language, and implement the theory that we present for GOAL. Several experiments with this implementation show that performance is in line with known results from traditional model checking

Toward social situation awareness in support agents

Artificial agents that support people in their daily activities (e.g., virtual coaches and personal assistants) are increasingly prevalent. Since many daily activities are social in nature, support agents should understand a user's social situation to offer comprehensive support. However, there are no systematic approaches for developing support agents that are social situation aware. We identify key requirements for a support agent to be social situation aware and propose steps to realize those requirements. These steps are presented through a conceptual architecture centered on two key ideas: 1) conceptualizing social situation awareness as an instantiation of "general" situation awareness, and 2) using situation taxonomies for such instantiation. This enables support agents to represent a user's social situation, comprehend its meaning, and assess its impact on the user's behavior. We discuss empirical results supporting the effectiveness of the proposed approach and illustrate howthe architecture can be used in support agents through two use cases.Algorithms and the Foundations of Software technolog

Grouping Situations Based on their Psychological Characteristics Gives Insight into Personal Values

Support agents are investigated more and more as a way of assisting people in carrying out daily tasks. Support agents should be flexible in adapting their support to what their user needs. Research suggests that the situation someone is in affects their behaviour, however its effect has not been incorporated in the decision making of support agents. Modelling the characteristics of situations explicitly and studying their effect on internal perceptions of the user, such as their personal values, would enable support agents to provide more personalized support. We propose a method which groups situations according to their psychological characteristics, and in turn determines which personal values of the user would be promoted or demoted in each group of situations. To do this, we conduct a user study to gather data from participants about situations that they encounter in their daily lives. Results show that the created groups of situations significantly promote or demote certain personal values. This approach can allow support agents to help the user in a way which is in line with their personal values

Socially adaptive electronic partners for improved support of children's values: An empirical study with a location-sharing mobile app

Mobile location-sharing technology is increasingly being used by parents to locate their children. Research shows that these technologies may pose risks to important user values such as privacy and responsibility, while they aim to promote others such as family security. As a solution, we proposed the use of Social Commitment (SC) models for governing the sharing and receiving of data. A social commitment represents an agreement between two people about which data should (not) be shared and received in which situation. We hypothesize that the use of SCs in mobile location sharing applications provides improved support for user values since it allows for a more flexible, context-aware location sharing. In this paper, we present a user study to test this hypothesis. The study focuses on primary school children ([Formula presented]) as the main target group, who's values may be demoted through the use of location-sharing technology. Children were provided with two versions of a mobile location sharing app: one with basic check-in functionality –the basic app –and one augmented with an SC model, which we call a Socially Adaptive Electronic Partner (SAEP). Our findings suggest, among other things that the SAEP would provide improved support for children's values compared to the basic app.</p

Automatic Resolution of Normative Conflicts in Supportive Technology Based on User Values

Social commitments (SCs) provide a flexible, norm-based, governance structure for sharing and receiving data. However, users of data sharing applications can subscribe to multiple SCs, possibly producing opposing sharing and receiving requirements. We propose resolving such conflicts automatically through a conflict resolution model based on relevant user values such as privacy and safety. The model predicts a user’s preferred resolution by choosing the commitment that best supports the user’s values. We show through an empirical user study (n = 396) that values, as well as recency and norm type, significantly improve a system’s ability to predict user preference in location sharing conflicts