Natural world interaction (NWI), the pursuit of arbitrary goals in unstructured physical environments, is an excellent motivating problem for the reintegration of artificial intelligence. It is the problem set that humans struggle to solve. At a minimum it entails perception, learning, planning, and control, and can also involve language and social behavior. An agent's fitness in NWI is achieved by being able to perform a wide variety of tasks, rather than being able to excel at one. In an attempt to address NWI, a brain-emulating cognition and control architecture (BECCA) was developed. It uses a combination of feature creation and model-based reinforcement learning to capture structure in the environment in order to maximize reward. BECCA avoids making common assumptions about its world, such as stationarity, determinism, and the Markov assumption. BECCA has been demonstrated performing a set of tasks which is non-trivially broad, including a vision-based robotics task. Current development activity is focused on applying BECCA to the problem of general Search and Retrieve, a representative natural world interaction task.

The fragmentation of research in AI and robotics has created a vast repertoire of skills a robot could be equipped with but that must be manually integrated to form a complex action. We propose a novel evolutionary algorithm that aims at autonomously integrating, adapting and creating new actions by re-using skills that are either externally provided or previously generated. Complex actions are created by instantiating a Finite State Automaton and new skills are created using fully recurrent neural networks. We validated our approach in two scenarios, i.e. exploration and moving to pre-grasp positions. Our experiments show that complex actions can be created by composing independently developed skills. The results have been applied and tested with a real robot in a variety of scenarios.

This paper gives a brief retrospective of a research project begun in 1987 and continuing to the present on the topic of language acquisition by an autonomous humanoid robot. We recount the motivations for, theoretical bases of and experimental results on this subject. Important results include novel models and algorithms resulting in interesting linguistic function of our robots.

The paper describes ongoing integrated research on designing intelligent robots that can assist humans in making a situation assessment during Urban Search & Rescue (USAR) missions. 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 dangerous or too difficult for a human to enter at that point. This requires the robots to be "intelligent" in the sense of being capable 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 interdependent. 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 control) 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 various insights from robotics and Artificial Intelligence are combined, to develop new approaches for modeling human robot teaming. These approaches range from new forms of modeling situation awareness (to model distributed acting in dynamic space), human robot interaction (to model communication in teams), flexible planning (to model team coordination and joint action), and cognitive system design (to integrate different forms of functionality in a single system).

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.

This paper describes an attempt to provide more intelligence to industrial robotics and automation systems. We develop an architecture to integrate disparate knowledge representations used in different places in robotics and automation. This knowledge integration framework, a possibly distributed entity, abstracts the components used in design or production as data sources, and provides a uniform access to them via standard interfaces. Representation is based on the ontology formalizing the process, product and resource triangle, where skills are considered the common element of the three. Production knowledge is being collected now and a preliminary version of KIF undergoes verification.

This paper provides a formal analysis of a multi-agent task allocationproblem and how variation in agent behavior in the form of responseprobabilities can be used to build redundancy in the multi-agent system (MAS).In problems where experience is beneficial redundancy provides an MASwith a back-up pool of actors if the primary actors are unavailable.We examine how to ensure a complete team of agents needed fora particular task will be formed, as well as two different ways ofdetermining how to ensure some level of redundancy.

In all of these problems, we have limited be done; yet these are large-scale interdisciplinary research security resources which prevent full security coverage challenges that call upon multiagent researchers to work at all times; instead, limited security resources must be deployed with researchers in other disciplines, be "on the ground" intelligently taking into account differences in priorities with domain experts, and examine real-world constraints of targets requiring security coverage, the responses of and challenges that cannot be abstracted away. Together as the adversaries to the security posture and potential uncertainty an international community of multiagent researchers, we over the types, capabilities, knowledge and priorities can accomplish more! of adversaries faced.

The importance of diversity in reasoning and learning to successfully address complex problems is examined. We discuss an approach by which a multiagent framework with decentralized control mechanisms provides diverse perspectives and hypotheses addressing a class of complex problems. We introduce the SNARE multiagent system. SNARE performs tasks to gain situational awareness of situations of interest in a Social Media Space. It applies a decentralized control mechanism for each agent; this mechanism enables an agent to interact with other agents to reason and learn. This approach facilitates dynamic agent organizations that adapt the topologies of interactions between agents based on the problem context.

Traditional social science research has been unable to satisfactorily aggregate individual level data to group, organization and systems levels, making it one of social science’s biggest challenges (Giles, 2011). For game and social theory, we believe that the fault can be attributed to the lack of valid distance measures (e.g., the arbitrary ordering of cooperation and competition precludes a Hilbert space distance metric for the ordering of and gradations between these social behaviors, making game theory normative). Alternatively, we offer a theory of social interdependence with countable mathematics based on bistable or multi-stable perspectives and linear algebra. The evidence that is available is supportive. It indicates that meaning is a one-sided, stable, classical interpretation, not only making the correspondence between beliefs and objective reality in social settings incomplete, raising questioning about static theories from earlier eras (i.e., Axelrod’s evolution of cooperation; Simon’s bounded rationality). The result indicates for open systems (democracies) that interpretations evolve naturally to become orthogonal (Nash equilibria), that orthogonal interpretations generate the information to drive social evolution, but that in closed systems (dictatorships), dependent on the enforcement of social cooperation and the suppression of opposing points of view, evolution slows or stops (e.g., China, Iran or Cuba), causing capital and energy to be wasted, misdirected or misallocated as leaders suppress the interpretations that they alone have the authority to label as unethical, immoral, or irreligious. We conclude that a mathematics based on NE is feasible.