A Game-Theoretic Approach to Timeline-Based Planning with Uncertainty

In timeline-based planning, domains are described as sets of independent, but interacting, components, whose behaviour over time (the set of timelines) is governed by a set of temporal constraints. A distinguishing feature of timeline-based planning systems is the ability to integrate planning with execution by synthesising control strategies for flexible plans. However, flexible plans can only represent temporal uncertainty, while more complex forms of nondeterminism are needed to deal with a wider range of realistic problems. In this paper, we propose a novel game-theoretic approach to timeline-based planning problems, generalising the state of the art while uniformly handling temporal uncertainty and nondeterminism. We define a general concept of timeline-based game and we show that the notion of winning strategy for these games is strictly more general than that of control strategy for dynamically controllable flexible plans. Moreover, we show that the problem of establishing the existence of such winning strategies is decidable using a doubly exponential amount of space

Multi-robot systems in cognitive factories: representation, reasoning, execution and monitoring

We propose the use of causality-based formal representation and automated reasoning methods from artificial intelligence to endow multiple teams of robots in a factory, with high-level cognitive capabilities, such as optimal planning and diagnostic reasoning. We present a framework that features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning. We embed this planning framework inside an execution and monitoring framework and show its applicability on multi-robot systems. In particular, we focus on two domains that are relevant to cognitive factories: i) a manipulation domain with multiple robots working concurrently / co-operatively to achieve a common goal and ii) a factory domain with multiple teams of robots utilizing shared resources. In the manipulation domain two pantograph robots perform a complex task that requires true concurrency. The monitoring framework checks plan execution for two sorts of failures: collisions with unknown obstacles and change of the world due to human interventions. Depending on the cause of the failures, recovery is done by calling the motion planner (to find a different trajectory) or the causal reasoner (to find a new task plan). Therefore, recovery relies on not only motion planning but also causal reasoning. We extend our planning and monitoring framework for the factory domain with multiple teams of robots by introducing algorithms for finding optimal decoupled plans and diagnosing the cause of a failure/discrepancy (e.g., robots may get broken or tasks may get reassigned to teams). We show the applicability of these algorithms on an intelligent factory scenario through dynamic simulations and physical experiments

Neues Konzept zur Planung, Ausführung und Überwachung von Roboteraufgaben mit hierarchischen Petri-Netzen

Es wird gezeigt, wie die aufgabenausführungsrelevanten Komponenten einer hybriden Steuerungsarchitektur mit Hilfe von hierarchischen Petri-Netzen umgesetzt, integriert und mit Überwachungsmodulen verknüpft werden können. Hierzu wird zunächst ein Konzept zur Generierung von Aufgabenwissen vorgeschlagen, das es erlaubt Bausteine komplexer Handlungen systematisiert zu entwerfen. Im Anschluss wird ein neues Konzept zur online Überwachung von Bewegungsvorgängen bei humanoiden Robotern vorgestellt

The Architect's Collaborator: Toward Intelligent Tools for Conceptual Design

In early stages of architectural design, as in other design domains, the language used is often very abstract. In architectural design, for example, architects and their clients use experiential terms such as "private" or "open" to describe spaces. If we are to build programs that can help designers during this early-stage design, we must give those programs the capability to deal with concepts on the level of such abstractions. The work reported in this thesis sought to do that, focusing on two key questions: How are abstract terms such as "private" and "open" translated into physical form? How might one build a tool to assist designers with this process? The Architect's Collaborator (TAC) was built to explore these issues. It is a design assistant that supports iterative design refinement, and that represents and reasons about how experiential qualities are manifested in physical form. Given a starting design and a set of design goals, TAC explores the space of possible designs in search of solutions that satisfy the goals. It employs a strategy we've called dependency-directed redesign: it evaluates a design with respect to a set of goals, then uses an explanation of the evaluation to guide proposal and refinement of repair suggestions; it then carries out the repair suggestions to create new designs. A series of experiments was run to study TAC's behavior. Issues of control structure, goal set size, goal order, and modification operator capabilities were explored. In addition, TAC's use as a design assistant was studied in an experiment using a house in the process of being redesigned. TAC's use as an analysis tool was studied in an experiment using Frank Lloyd Wright's Prairie houses

Improving Robot Plans During Their Execution

We describe how our planner, xfrm, carries out the process of anticipating and forestalling execution failures. xfrm is a planning system that is embedded in a simulated robot performing a varying set of complex tasks in a changing and partially unknown environment. xfrm revises plans controlling the robot while they are executed. Thus whenever the robot detects a contingency, xfrm projects the effects of the contingency on its plan and---if necessary---revises its plan in order to make it more robust. Using xfrm, the robot can perform its tasks almost as efficiently as it could using efficient default plans, but much more robustly. Revising default plans requires xfrm to reason about full-fledged robot plans and diagnose various kinds of plan failures that might be caused by imperfect sensing and effecting, incomplete and faulty world models, and exogenous events. To this end, xfrm reasons about the structure, function, and behavior of plans, and diagnoses projected plan failures by..