User modeling and user adapted interaction

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Overview of fault diagnosis methods for Discrete Event Systems

In manufacturing, advanced robotic technology has opened up the possibility of integrating highly autonomous mobile robots into human teams. However, with this capability comes the issue of how to maximize both team efficiency and the desire of human team members to work with these robotic counterparts. To address this concern, we conducted a set of experiments studying the effects of shared decision-making authority in human---robot and human-only teams. We found that an autonomous robot can outperform a human worker in the execution of part or all of the process of task allocation ($$p<0.001$$p<0.001 for both), and that people preferred to cede their control authority to the robot $$(p<0.001)$$(p<0.001). We also established that people value human teammates more than robotic teammates; however, providing robots authority over team coordination more strongly improved the perceived value of these agents than giving similar authority to another human teammate $$(p< 0.001)$$(p<0.001). In post hoc analysis, we found that people were more likely to assign a disproportionate amount of the work to themselves when working with a robot $$(p<0.01)$$(p<0.01) rather than human teammates only. Based upon our findings, we provide design guidance for roboticists and industry practitioners to design robotic assistants for better integration into the human workplace.

/pdf/decision-making-authority-team-efficiency-and-human-worker-1cz2hqtifj.pdf

Decision-making authority, team efficiency and human worker satisfaction in mixed human---robot teams

Advancements in robotic technology are making it increasingly possible to integrate robots into the human workspace in order to improve productivity and decrease worker strain resulting from the pe...

https://journals.sagepub.com/doi/pdf/10.1177/0278364916688255

Computational design of mixed-initiative human–robot teaming that considers human factors: situational awareness, workload, and workflow preferences:

Since it is known that Model-Based Diagnosis may suffer from a potentially exponential size of the search space, a number of techniques have been proposed for alleviating the problem. Among them, some forms of compilation of the domain model have been investigated. In the present paper we address the problem of evaluating the complexity of diagnostic problem solving when Ordered Binary Decision Diagrams are adopted for representing the normal and faulty behavior of the system to be diagnosed and the solution space. In particular we analyze the case of the diagnosis of static models that exhibit a directionality from inputs to outputs (an important example of this type of models is the class of combinatorial digital circuits). We show that the problem of determining the set of all diagnoses and of determining the minimum cardinality diagnoses can be solved in time and space polynomial with respect to the size of the OBDD encoding the domain model. These results hold regardless of the degree of system observability including whether observations are precise or uncertain. We then analyze the complexity of refining the set of diagnoses by making additional observations and by using a test vector for troubleshooting the system. In particular we show that in the latter case we lose the formal guarantee that the diagnosis can be performed in polynomial time with respect to the size of the compiled domain model.

Model-Based diagnosis through OBDD compilation: a complexity analysis

The paper presents an approach for the monitoring and diagnosis of multi-agent systems where mobile robotic agents provide services and partial observability of the environment is achieved via a set of fixed sensors. This kind of systems exhibits complex dynamics where weakly predictable interactions among agents may arise. A model-based approach to on-line monitoring and diagnosis is adopted: while the dynamics of the system components and their relations are modeled via communicating automata, the global system model is factored in a number of subsystems dynamically aggregating a convenient set of component models.

The On-line Monitoring Module (OMM) estimates the possible evolutions of the system by exploiting partial system observability provided by sensors and agents messages and enforces global constraints. When the monitor detects failures in the actions execution, the Diagnostic Interpretation Module (DIM) is triggered for explaining the failure in terms of faults in the robotic agents and/or troublesome interactions among them. As a specific case-study we refer to the RoboCare project2.

On-line monitoring and diagnosis of multi-agent systems: a model based approach

The paper presents an approach for the on-line monitoring and diagnosis of multi-robot systems where services are provided by a team of robots and the environment is only partially observable via a net of fixed sensors. This kind of systems exhibits complex dynamics where weakly predictable interactions among robots may occur. To face this problem, a model-based approach is adopted: in particular, the paper discusses how to build a system model by aggregating a convenient set of basic system components, which are modeled via communicating automata. Since the dynamics of a multi-robot system depend on the actions performed by the robots (and actions change over time), the global system model is partitioned into a number of submodels, each one describing the dynamics of a single action.The paper introduces the architecture of the Supervisor which has to track the actions progress and to infer an explanation when an action is completed with delay or fails. The Supervisor includes two main modules: the On-line Monitoring Module (OMM) tracks the status of the system by exploiting the (partial) observations provided by sensors and robots. When the monitor detects failures in the actions execution, the Diagnostic Interpretation Module (DIM) is triggered for explaining the failure in terms of faults in the robots and/or troublesome interactions among them.The RoboCare domain has been selected as a test bed of the approach. The paper discusses experimental results collected in such a domain with particular focus on the competence and the efficiency of both the OMM and the DIM.

On-line monitoring and diagnosis of a team of service robots: A model-based approach

The paper addresses the problem of solving diagnostic problems by exploiting OBDDs (Ordered Binary Decision Diagrams) as a way for compactly representing the set of alternative diagnoses. In the MBD (Model Based Diagnosis) community it is indeed well known that the number of diagnoses can be exponential in the system size even when restricted to preferred diagnoses (e.g. minimal diagnoses). In particular, the paper presents methods and heuristics for efficiently encoding the domain theory of the system model in terms of an OBDD. Such heuristics suggest suitable ordering of the OBDD variables which prevents the explosion of the OBDD size for some classes of domain theories. Moreover, we describe how to solve specific diagnostic problems represented as OBDDs and report some results on the computational complexity of such process. Finally, we introduce a mechanism for extracting diagnoses with the minimum number of faults from the OBDD which represents the entire space of diagnoses. Experimental results are collected and reported on a model representing a simplified propulsion subsystem of a spacecraft.

Computing Minimum-Cardinality Diagnoses Using OBDDs

The paper addresses the problem of automatic abstraction of component variables in the context of Model Based Diagnosis, in order to produce models capable of deriving fewer and more general diagnoses when the current observability of the system is reduced. The notion of indiscriminability among faults of a set of components is introduced and constitutes the basis for a formal definition of admissible abstractions which preserve all the distinctions that are relevant for diagnosis given the current observability of the system. The automatic synthesis of abstract models further restricts abstractions such that the behavior of abstract components is expressed in terms of a simple and intuitive combination of the behavior of their subcomponents. As a validation of our proposal, we present experimental results which show the reduction in the number of diagnoses returned by a diagnostic agent for a space robotic arm.

https://ijcai.org/Proceedings/03/Papers/059.pdf

Gianluca Torta

Papers

Model-Based diagnosis through OBDD compilation: a complexity analysis

On-line monitoring and diagnosis of multi-agent systems: a model based approach

On-line monitoring and diagnosis of a team of service robots: A model-based approach

Computing Minimum-Cardinality Diagnoses Using OBDDs

Automatic abstraction in component-based diagnosis driven by system observability