The paper describes ongoing integrated research on designing intelligent robots that can assist humans in making a situa-tion assessment during Urban Search & Rescue (USAR) mis-sions. These robots (rover, microcopter) are deployed during the early phases of an emergency response. The aim is to explore those areas of the disaster hotzone which are too dan-gerous or too difficult for a human to enter at that point. This requires the robots to be “intelligent ” in the sense of being ca-pable of various degrees of autonomy in acting and perceiving in the environment. At the same time, their intelligence needs to go beyond mere task-work. Robots and humans are inter-dependent. Human operators are dependent on these robots to provide information for a situation assessment. And robots are dependent on humans to help them operate (shared con-trol) and perceive (shared assessment) in what are typically highly dynamic, largely unknown environments. Robots and humans need to form a team. The paper describes how vari-ous insights from robotics and Artificial Intelligence are com-bined, to develop new approaches for modeling human robot teaming. These approaches range from new forms of mod-eling situation awareness (to model distributed acting in dy-namic space), human robot interaction (to model communi-cation in teams), flexible planning (to model team coordina-tion and joint action), and cognitive system design (to inte-grate different forms of functionality in a single system).

The paper addresses how a robot can maintain a state representation of all that it knows about the environment over time and space, given its observations and its domain knowledge. The advantage in combining domain knowledge and observations is that the robot can in this way project from the past into the future, and reason from observations to more general statements to help guide how it plans to act and interact. The difficulty lies in the fact that observations are typically uncertain and logical inference for completion against a knowledge base is computationally hard.

In this paper we examine the case of a mobile robot that is part of a human-robot urban search and rescue (USAR) team. During USAR scenarios, we would like the robot to have a geometrical-functional understand-ing of space, using which it can infer where to perform planned tasks in a manner that mimics human behav-ior. We assess the situation awareness of rescue work-ers during a simulated USAR scenario and use this as an empirical basis to build our robot’s spatial model. Based upon this spatial model, we present “functional map-ping ” as an approach to identify regions in the USAR environment where planned tasks are likely to be opti-mally achievable. The system is deployed and evaluated in a simulated rescue scenario.

Abstract — Extended logic programs (ELPs) are a set of logic rules with strong negation � allowed in the bodies or head of the rules and weak negation ~ allowed in the bodies of the rules. ELPs enable for various forms of reasoning that cannot be achieved by definite logic programs. Answer Set Programming provides a widely acceptable semantics for ELPs. However, ELPs do not provide information regarding the temporal intervals that derived ELP literals or weakly negated ELP literals are valid. In this paper, we associate ELP rules with their validity temporal interval, resulting in a temporally annotated logic program. A ground temporal literal has the form L:i, where L is a ground ELP literal or weakly negated ELP literal and i is a temporal interval. We define (simple) entailment and maximal entailment of a ground temporal literal L:i from a temporally annotated logic program C. Both kinds of entailment are based on Answer Set Programming. Additionally, we provide an algorithm that for an ELP literal or a weakly negated ELP literal L returns a list with all temporal intervals i such that a temporally annotated logic program C maximally entails L:i. Based on this algorithm, the answer of various kinds of temporal queries can be provided. Keywords- Extended logic programs; validity temporal intervals; temporal inference; query answering. I.

Abstract — In this paper, we provide a survey on the models and query languages for temporally annotated RDF. In most of the works, a temporally annotated RDF ontology is essentially a set of RDF triples associated with temporal constraints, where, in the simplest case, a temporal constraint is a validity temporal interval. However, a temporally annotated RDF ontology may also be a set of triples connecting resources with a specific lifespan, where each of these triples is also associated with a validity temporal interval. Further, a temporal RDF ontology may be a set of triples connecting resources as they stand at specific time points. Several query languages for temporally annotated RDF have been proposed, where most of which extend SPARQL or translate to SPARQL. Some of the works provide experimental results while the rest are purely theoretical. Keywords- Temporal RDF; provenance; semantics; query languages. I.