Oliver Obst

Specifying Rational Agents with Statecharts and Utility Functions

To aid the development of the robotic soccer simulation league team RoboLog-2000, a method for the specification of multi-agent teams by statecharts has been introduced. The results in the last years competitions showed that though the team was competitive, it did not behave adaptive in unknown situations. The design of adaptive agents with this method is possible, but not in a straightforward manner. The purpose of this paper is to extend the approach by a more adaptive action selection mechanism and to facilitate a more explicit representation of goals of an agent.

Qualitative Velocity and Ball Interception

In many approaches for qualitative spatial reasoning, navigation of an agent in a more or less static environment is considered (e.g. in the double-cross calculus [12]). However, in general, real environment are dynamic, which means that both the agent itself and also other objects and agents in the environment may move. Thus, in order to perform spatial reasoning, not only (qualitative) distance and orientation information is needed (as e.g. in [1]), but also information about (relative) velocity of objects (see e.g. [2]). Therefore, we will introduce concepts for qualitative and relative velocity: (quick) to left, neutral, (quick) to right. We investigate the usefulness of this approach in a case study, namely ball interception of simulated soccer agents in the RoboCup [10]. We compare a numerical approach where the interception point is computed exactly, a strategy based on reinforcement learning, a method with qualitative velocities developed in this paper, and the naive method where the agent simply goes directly to the actual ball position.

Towards a Logical Approach for Soccer Agents Engineering

Building agents for a scenario such as the RoboCup simulation league requires not only methodologies for implementing high-level complex behavior, but also the careful and efficient programming of low-level facilities like ball interception. With this hypothesis in mind, the development of RoboLog Koblenz has been continued. As before, the focus is laid on the declarativity of the approach. This means, agents are implemented in a logic- and rule-based manner in the high-level and flexible logic programming language Prolog. Logic is used as a control language for deciding how an agent should behave in a situation where there possibly is more than one choice. n nIn order to describe the more procedural aspects of the agent,s behavior, we employ state machines, which are represented by statecharts. Because of this, the script language for modeling multi-agent behavior in [8] has been revised, such that we are now able to specify plans with iterative parts and also reactive behavior, which is triggered by external events. In summary, multi-agent behavior can be described in a script language, where procedural aspects are specified by statecharts and declarative aspects by logical rules (in decision trees). Multi-agent scripts are implemented in Prolog. The RoboLog kernel is written in C++ and makes now use of the low-level skills of the CMUnited-99 simulator team.

Spatial Agents Implemented in a Logical Expressible Language

In this paper, we present a multi-layered architecture for spatial and temporal agents. The focus is laid on the declarativity of the approach, which makes agent scripts expressive and well understandable. They can be realized as (constraint) logic programs. The logical description language is able to express actions or plans for one and more autonomous and cooperating agents for the RoboCup (Simulator League). The system architecture hosts constraint technology for qualitative spatial reasoning, but quantitative data is taken into account, too. The basic (hardware) layer processes the agent’s sensor information. An interface transfers this low-level data into a logical representation. It provides facilities to access the preprocessed data and supplies several basic skills. The second layer performs (qualitative) spatial reasoning. On top of this, the third layer enables more complex skills such as passing, offside-detection etc. At last, the fourth layer establishes acting as a team both by emergent and explicit cooperation. Logic and deduction provide a clean means to specify and also to implement teamwork behavior.

Towards a league-independent qualitative soccer theory for robocup

The paper discusses a top-down approach to model soccer knowledge, as it can be found in soccer theory books. The goal is to model soccer strategies and tactics in a way that they are usable for multiple RoboCup soccer leagues, i.e. for different hardware platforms. We investigate if and how soccer theory can be formalized such that specification and execution is possible. The advantage is clear: theory abstracts from hardware and from specific situations in leagues. We introduce basic primitives compliant with the terminology known in soccer theory, discuss an example on an abstract level and formalize it. We then consider aspects of different RoboCup leagues in a case study and examine how examples can be instantiated in three different leagues.

Simulation League: The Next Generation

We present a modular approach to model multi-agent simulations in 3D environments. Using this approach, we implemented a generic simulator which is totally decoupled from the actual simulation it performs. We believe that for Soccer Simulation League a transition to 3D states exiting new research problems and equally makes it more attractive to watch for spectators. We are proposing to use our framework as basis for a next generation Soccer Server.

Using Model-Based Diagnosis to Build Hypotheses about Spatial Environments

We present a method to build a hypothesis on the condition of the environment in which a robotic multi-agent team moves. Initially the robots have a default assumption about the conditions of the floor and on how moving under these condition works. For certain parts of the environment however, the default assumption may be wrong and moving around does not work in the expected way. Now the robotic team builds a hypothesis on the conditions of the yet unvisited part of the environment in a way similar to computing a diagnosis for electrical circuits. Resources can be saved by avoiding areas that possibly also contain obstacles.

Flexible coordination of multiagent team behavior using HTN planning

The domain of robotic soccer is known as a highly dynamic and non-deterministic environment for multiagent research. We introduce an approach using Hierarchical Task Network planning in each of the agents for high-level coordination and description of team strategies. Our approach facilitates the maintenance of expert knowledge specified as team strategies separated from the agent implementation. By combining high level plans with reactive basic operators, agents can pursue a grand strategy while staying reactive to changes in the environment. Our results show that the use of a planner in a multiagent system is both possible and useful despite the constraints in dynamic environments.

Spark – A Generic Simulator for Physical Multi-agent Simulations

In this paper we describe a new multi-agent simulation system, called Spark, for physical agents in three-dimensional environments. Our goal in creating Spark was to provide a great amount of flexibility for creating new types of agents and simulations. To achieve this, we implemented a flexible application framework and exhausted the idea of replaceable components in the resulting system. In comparison to specialized simulators, users can effortlessly create new simulations by using a scene description language. Spark is a powerful and flexible tool to state different multi-agent research questions. It is used as official simulator for the first three-dimensional RoboCup Simulation League competition. We present the concepts we used to achieve the flexibility in our system and show how we seamlessly integrated the different subsystems into one user-friendly framework.

An overview of RoboCup-2002 Fukuoka/Busan

This article reports on the Sixth Robot World Cup Competition and Conference (RoboCup-2002) Fukuoka/Busan, which took place from 19 to 25 June in Fukuoka, Japan. It was the largest Robo-Cup since 1997 and held the first humanoid league competition in the world. Further, the first ROBOTREX (robot trade and exhibitions) was held with about 50 companies, universities, and institutes represented. A total of 117,000 spectators witnessed this marvelous event. To the best of our knowledge, this was the largest robotic event in history.