Johannes Fähndrich

Towards a Holistic Approach for Problems in the Energy and Mobility Domain

Abstract With the current rise of electric vehicles, it is possible to use those vehicles for storing surplus energy from renewable energy sources; however, this can be in conflict with providing and ensuring the mobility of the vehicles user. At DAI-Labor, we have a large number of both, past and upcoming projects concerned with those two aspects of managing electric vehicles: energy and mobility. To unify and facilitate developments in those projects, we developed common domain models describing the different aspects of the e-mobility domain. Those domain models are used in many of our projects for optimising charging schedules and for ensuring the users mobility.

Ants in the OCEAN: Modulating Agents with Personality for Planning with Humans

This work introduces a prototype that demonstrates the idea of using a psychological theory of personality types known as the Five-Factor Model (FFM) in planning for human-agent teamwork scenarios. FFM is integrated into the BDI model of agency leading to variations in the interpretation of inputs, the decision-making process and the generation of outputs. This is demonstrated in a multi-agent simulation. Furthermore, it is outlined how these variations can be used for the planning process in collaborative settings.

A common approach to intelligent energy and mobility services in a smart city environment

Due to the fact that electric vehicles have not broadly entered the vehicle market there are many attempts to convince producers to integrate technologies that utilise embedded batteries for purposes different from driving. The vehicle-to-grid technology, for instance, literally turns electric vehicles into a mobile battery, enabling new areas of applications (e.g., to provide regulatory energy, to do grid-load balancing, or to buffer surpluses of energy) and business perspectives. Utilising a vehicle’s battery, however is not without a price—in this case: the driver’s mobility. Given this dependency, it is interesting that most available works consider the application of electric vehicles for energy and grid-related problems in isolation, that is, detached from mobility-related issues. The distributed artificial intelligence laboratory, or DAI-Lab, is a third-party funded research lab at Technische Universität Berlin and integrates the chair for agent technologies in business applications and telecommunication. The DAI-Lab has engaged in a large number of both, past and upcoming projects concerned with two aspects of managing electric vehicles, namely: energy and mobility. This article aims to summarise experiences that were collected during the last years and to present developed solutions which consider energy and mobility-related problems jointly.

Towards Self-Explaining Agents

We advocate Self-Explanation as the foundation for the Self-* properties. Arguing that for system component to have such properties the underlining foundation is a awareness of them selfs and their environment. In the research area of adaptive software, self-* properties have shifted into focus pushing ever more design decisions to a applications runtime. Thus fostering new paradigms for system development like intelligent agents. This work surveys the state of the art methods of self-explanation in software systems and distills a definition of self-explanation.

HPLAN: Facilitating the Implementation of Joint Human-Agent Activities

When it comes to planning for joint human-agent activities, one has to consider not only flexible plan execution and social constraints but also the dynamic nature of humans. This can be achieved by providing additional information about the characteristics of a human. As an example one need to take the physical and psychological condition of the elderly into consideration when developing collaborative applications like socially assistive robots. This work outlines Hplan, an extension to the agent-framework JIAC V, that takes this requirement into account. Hplan is strongly related to the conceptual model of dynamic planning components and integrates humans as avatars into a life cycle of planning, execution and learning.

Preventing Elderly from Falls: The Agent Perspective in EPRs

This work presents an approach combining multiple electronic patient records (EPR) to a self-learning fall risk assessment tool. We utilized the agent-perspective to model the system, to address privacy issues and to evaluate different distributed information fusion and opinion aggregation techniques towards there applicability to the addressed domain. Each agent represents a single patient negotiating about unknown fall risk influences in order to adapt the fall-risk assessment tool to the population under care. In addition, we will outline the planned real-world case study.

Towards Automated Service Matchmaking and Planning for Multi-Agent Systems with OWL-S – Approach and Challenges

In the past, the demand for modular, distributed and dynamic computer systems has increased rapidly. In the field of multi-agent systems (MAS) many of the current approaches try to account for these requirements. In this paper we discuss the shortcomings of the semantic service selection component SeMa2, propose improvements and describe an integration concept into a multi-agent framework. Further, we illustrate how this system can be extended by an automated service composition component using methods from the AI planning community.

Human-Aware Planning: A Survey Related to Joint Human-Agent Activities

To become a part of a joint human-agent team, artificial agent are required to achieve joint goals with humans not only performing task for humans. This includes the ability to coordinate actions between team-members, which is e.g. addressed by Human-Aware Planning approaches. This work surveys available solutions regarding the special requirements identified for joint human-agent activities. In particular, the work concentrates on the requirement of interpredictability, which requires to include the course of actions of other team-members into the planning process of one’s own course of action.

Formal Language Decomposition into Semantic Primes

This paper describes an algorithm for semantic decomposition. For that we surveys languages used to enrich contextual information with semantic descriptions. Such descriptions can be e.g. applied to enable reasoning when collecting vast amounts of information. In particular, we focus on the elements of the languages that make up their semantic. To do so, we compare the expressiveness of the well-known languages OWL, PDDL and MOF with a theory from linguistic called the Natural Semantic Metalanguage. We then analyze how the semantic of the language is build up and describe how semantic decomposition based on the semantic primes can be used for a so called mental lexicon. This mental lexicon can be used to reason upon semantic service description in the research domain of service match making.

An Agent-Based Augmented Reality Demonstrator in the Domestic Energy Domain

In this work we propose an approach for comfortable and accelerated development of user interfaces for software agents.We apply model-based techniques and emphasise the capability of this technique by describing two user interfaces which are different in nature, but have been developed with the same model. We present the applicability of both user interfaces by means of an agent-based application in the domestic energy domain. As opposed to similar approaches we retain all degrees of freedom for the applied multi-agent framework.