One of NIST's research areas has been the quantification of Each team's system is faced with challenges such as The goal of ARIAC is to solidify the shown in figure 1. The organizers chose kitting field of robot agility, while also progressing the state because of its similarity to assembly. Teams were tasked with assembling a robotic system's (robot, controller, and sensors) ability kit both from bins of stationary parts and from a to respond to a dynamic environment. After the robotic system finished dynamic response includes handling errors like the kit, the kit was placed on an autonomous guided dropped parts or responding to changes in orders, all vehicle (AGV) and taken away. Teams were faced with such challenges as forced The competition addresses the aspect of robot dropped parts and in-process order changes.

The action programming language GOLOG has been found useful for the control of autonomous agents such as mobile robots. In scenarios like these, tasks are often open-ended so that the respective control programs are non-terminating. Before deploying such programs on a robot, it is often desirable to verify that they meet certain requirements. For this purpose, Claßen and Lakemeyer recently introduced algorithms for the verification of temporal properties of GOLOG programs. However, given the expressiveness of GOLOG, their verification procedures are not guaranteed to terminate. In this paper, we show how decidability can be obtained by suitably restricting the underlying base logic, the effect axioms for primitive actions, and the use of actions within GOLOG programs. Moreover, we show that dropping any of these restrictions immediately leads to undecidability of the verification problem.

Our understanding of the notion "dynamic system" is a rather broad one: such a system has states, which can change over time. Ontologies are used to describe the states of the system, possibly in an incomplete way. Monitoring is then concerned with deciding whether some run of the system or all of its runs satisfy a certain property, which can be expressed by a formula of an appropriate temporal logic. We consider different instances of this broad framework, which can roughly be classified into two cases. In one instance, the system is assumed to be a black box, whose inner working is not known, but whose states can be (partially) observed during a run of the system. In the second instance, one has (partial) knowledge about the inner working of the system, which provides information on which runs of the system are possible. In this paper, we will review some of our recent work that can be seen as instances of this general framework of ontology-based monitoring of dynamic systems. We will also mention possible extensions towards probabilistic reasoning and the integration of mathematical modeling of dynamical systems.

In this paper we contribute to bridging the gap between human reasoning as studied in Cognitive Science and commonsense reasoning based on formal logics and formal theories. Stenning and van Lambalgen presented an approach to model human reasoning by means of logic programs. In this paper, we extend a refined version of their approach by abduction and demonstrate that this permits to adequately model various empiric results on the suppression task reported from Cognitive Science.