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RoboCup was created in 1996 by a group of Japanese, American, and European artificial intelligence and robotics researchers with a formidable, visionary long-term challenge: By 2050 a team of robot [...]

Effective team strategies and joint decision-making processes are fundamental in modern robotic applications, where multiple units have to cooperate to achieve a common goal. The research community in artificial intelligence and robotics has launched robotic competitions to promote research and validate new approaches, by providing robust benchmarks to evaluate all the components of a multiagent system—ranging from hardware to high-level strategy learning. Among these competitions RoboCup has a prominent role, running one of the first worldwide multirobot competition (in the late 1990s), challenging researchers to develop robotic systems able to compete in the game of soccer. Robotic soccer teams are complex multirobot systems, where each unit shows individual skills, and solid teamwork by exchanging information about their local perceptions and intentions. In this survey, we dive into the techniques developed within the RoboCup framework by analyzing and commenting on them in detail. We highlight significant trends in the research conducted in the field and to provide commentaries and insights, about challenges and achievements in generating decision-making processes for multirobot adversarial scenarios. As an outcome, we provide an overview a body of work that lies at the intersection of three disciplines: Artificial intelligence, robotics, and games.

The creation of intelligent robots has been a major goal of Artificial Intelligence since the early days and has provided many motivations to Artificial Intelligence researchers. Therefore, a large body of research has been done in this field and many relevant results have shown that integration of Artificial Intelligence and Robotics techniques is a viable approach towards this goal. This article summarizes the efforts and the achievements of several Italian research groups in the development of intelligent robotic systems characterized by a suitable integration of Artificial Intelligence and Robotic techniques. The contributions collected in this article show the long history of this research stream, the impact of the developed approaches in the scientific community, and the efforts towards actual deployment of the developed systems.

Personal narratives are presented about the authors' climbs in mountains in Nepal including Changla, Kojichuwa Chuli, and Tsartse.

Personal narratives are presented about the authors' climbs in mountains in Pakistan including Chiche Peak, Chashkin Sar, and Gasherbrum II.

Coordination in multi-robot systems is a very active research field in Artificial Intelligence and Robotics, since through coordination one can achieve a more effective execution of the robots' tasks. In this paper we present an approach to distributed coordination of a multi-robot system that is based on dynamic role assignment. The approach relies on the broadcast communication of utility functionsthat define the capability for every robot to perform a task and on the execution of a coordination protocol for dynamic role assignment. The presented method is robust to communication failures and suitable for application in dynamic environments. In addition to experimental results showing the effectiveness of our approach, the method has been successfully implemented within the team of heterogeneous robots Azzurra Robot Team in a very dynamic hostile environment provided by the RoboCup robotic soccer competitions.

We present description logics of minimal knowledge and negation as failure (MKNF-DLs), which augment description logics with modal operators interpreted according to Lifschitz's nonmonotonic logic MKNF. We show the usefulness of MKNF-DLs for a formal characterization of a wide variety of nonmonotonic features that are both commonly available inframe-based systems, and needed in the development of practical knowledge-based applications: defaults, integrity constraints, role, and concept closure. In addition, we provide a correct and terminating calculus for query answering in a very expressive MKNF-DL.

We present an integrated system for knowledge representation, calledAL-log, based on description logics and the deductive database language Datalog. AL-log embodies two subsystems, called structural and relational. The former allows for the definition of structural knowledge about classes of interest (concepts) and membership relation between objects and classes. The latter allows for the definition of relational knowledge about objects described in the structural component. The interaction between the two components is obtained by allowing constraints within Datalog clauses, thus requiring the variables in the clauses to range over the set of instances of a specified concept. We propose a method for query answering in AL-log based on constrained resolution, where the usual deduction procedure defined for Datalog is integrated with a method for reasoning on the structural knowledge.

The basic feature of Hybrid Knowledge Representation Systems is the separation of the knowledge represented within the system into distinct components. In particular, the architecture of several such systems includes two components: terminological, containing the knowledge about the classes of objects and their relationships, and assertional, with the knowledge about the individuals. The aim of this work is to study how the deduction methods developed for the terminological component can be effectively used in the hybrid deductions on the whole knowledge base. We consider a hybrid system, whose terminological component provides the user with a quite powerful language, while remaining computationally tractable, and whose assertional component simply allows one to express membership assertions between the individuals and the concepts of the terminology. In this paper, we present the languages for the two components, define the semantics of the whole knowledge base, and describe an efficient technique for the implementation of hybrid reasoning procedures, based on the notion of most specific concept.

In this paper we address the problem of building tools for improving the software engineering process by taking advantage of artificial intelligence techniques. More specifically, we provide a representation of the evolution of a software project by means of temporal logics. Such a representation is the basis for the construction of tools for the control and management of a software project. A system for Access and Version Control (SCAV) provides a suitable test bed for our approach. In the paper we present the formalization of the history of a software project developed under SCAV, and we show how several kinds of functionalities can be easily realized. The formalization is built within the framework of the event calculus in such a way that every step of the project development determined by the execution of a SCAV operation is described as an event in the calculus. We present an implementation of the system in PROLOG, which allows for a direct and natural formulation of the event calculus rules.

In this paper we discuss recent developments in the research on knowledge representation, focusing on hybrid formalisms, nonmonotonic reasoning, and formalisms for reasoning about knowledge and reasoning in a multiagent scenario.

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the meta-level approach to reasoning about knowledge and reasoning.