Synthesis method of strategy for multi-robot systems from local automatons under probabilistic model checking

Through innovatively introducing the receding horizon into probabilistic model checking, an online strategy synthesis method for multi-robot systems from local automatons is proposed to complete complex tasks that are assigned to each robot. Firstly, each robot is modeled as a Markov decision process which models both probabilistic and nondeterministic behavior. Secondly, the task specification of each robot is expressed as a linear temporal logic formula. For some tasks that robots cannot complete by themselves, the collaboration requirements take the form of atomic proposition into the LTL specifications. And the LTL specifications are transformed to deterministic rabin automatons over which a task progression metric is defined to determine the local goal states in the finite-horizon product systems. Thirdly, two horizons are set to determine the running steps in automatons and MDPs. By dynamically building local finite-horizon product systems, the collaboration strategies are synthesized iteratively for each robot to satisfy the task specifications with maximum probability. Finally, through simulation experiments in an indoor environment, the results show that the method can synthesize correct strategies online for multi-robot systems which has no restriction on the LTL operators and reduce the computational burden brought by the automaton-based approach.

Building the Foundation of Robot Explanation Generation Using Behavior Trees

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

Conceptualizing change in organizational cognition

PurposeSecchi and Cowley (2016, 2018) propose a Radical approach to Organizational Cognition (ROC) as a way of studying cognitive processes in organizations. What distinguishes ROC from the established research on Organizational Cognition is that it remains faithful to radical, anti-representationalist principles of contemporary cognitive science. However, it is imperative for proponents of ROC to legitimize their approach by considering how it differs from the established research approach of Distributed Cognition (DCog). DCog is a potential contender to ROC in that it not only counters classical approaches to cognition but also provides valuable insights into cognition in organizational settings.Design/methodology/approachThe paper adopts a conceptual/theoretical approach that expands Secchi and Cowley's introduction of ROC.FindingsThe paper shows that DCog research presupposes a task-specification requirement, which entails that cognitive tasks are well-defined. Consequently, DCog research neglects cases of organizational becoming where tasks cannot be clearly demarcated for the or are well-known to the organization. This is the case with the introduction of novel tasks or technical devices. Moreover, the paper elaborates on ROC's 3M model by linking it with insights from the literature on organizational change. Thus, it explores how organizing can be explored as an emergent phenomenon that involves micro, meso and macro domain dynamics, which are shaped by synoptic and performative changes.Originality/valueThe present paper explores new grounds for ROC by not only expanding on its core model but also showing its potential for informing organizational theory and radical cognitive science research.

The Power of Many

Collective efforts of masses provide access to funding and ideas. While such endeavors in a business-to-customer context are well-described, they are less well understood in other contexts such as business-to-business. A literature review that exacts knowledge and inspiration from B2C crowdsourcing and other forms of collective innovation is used. This review generates new knowledge to close this gap and develops a 6-stage innovation framework for Collective Engagement, Intelligence & Innovation (CEI^2) that begins with task specification and concludes with management of inputs generated from the CEI^2 efforts. The framework and the accompanying list of questions may be used by theorists to explore different contexts, and for managers to structure B2B or P2P crowdsourcing more effectively. Contributions of this study include exploration of the theoretical areas of open-source innovation that extend beyond a B2C model, and new ways of effectively structuring CEI^2. Further research may explore the CEI^2 framework through a case study or test it through quantitative study.

Semantic Restful Service Composition Using Task Specification

Existing Web API search engines allow only category-based browsing and keyword- or tag-based searches for RESTful services. In other words, they do not enable the discovery or composition of real-world RESTful services by application developers. This paper outlines a novel scheme, called Transformation–Annotation–Discovery (TAD), which transforms OpenAPI (Swagger) documents related to RESTful services into a graph structure and then automatically annotates the semantic concepts on graph nodes using Latent Dirichlet Allocation (LDA) and WordNet. TAD can then be used for service composition based on the user requirements specified in two modules: a service discovery chain and logical-operation-based composition. The service discovery chain uses the Hungarian algorithm to assess service interface compatibility in order to facilitate the retrieval of services capable of bridging the gap between specified user requirements and the discovered services. The logical-operation-based composition module identifies services that semantically fit the user requirements, based on the structure of the service flow. Those candidate services are then sent to service discovery chains to enable the simultaneous search for potential composition solutions. System prototype and experiment results demonstrate the feasibility and efficacy of the proposed scheme.