Continual On-line Planning

Abstract My research proposes an approach to integrating planning and execution in time-sensitive environments, a setting I call continual on-line planning. New goals arrive stochastically during execution, the agent issues actions for execution one at a time, and the environment is otherwise deterministic. My dissertation will address this setting in three stages: optimizing total goal achievement time, handling on-line goal arrival, and adapting to exogenous changes in state. My approach to these problems is based on incremental heuristic search. The two central issues are the decision of which partial plans to elaborate during search and the decision of when to issue an action for execution. I propose an extension of Russell and Wefald's decision-theoretic A* algorithm to inadmissible heuristics. This algorithm, Decision Theoretic On-line Continual Search (DTOCS), handles the complexities of the on-line setting by balancing deliberative planning and realtime response

A Preventive Maintenance Framework in Dairy Production Operations

Dairy operations suffer frequent stops. Product shrinkage is a consequence of downtime, which includes losses of packaging material, scraped finish product and capacity. This work proposes a troubleshooting methodology to identify causes of downtime, estimation of waste cost, and minimization of operation disruptions by applying a combination of a cost function to assess waste, and performance measurements. The drinkable yogurt process is evaluated to find the principal areas for wasted bottles and yogurt. In order to make a decision about which of those sources to address, a General Cost Function is used to estimate waste cost which include measurements that evaluate the entire process. Further performance measure analysis such as Squared Coefficient of Variation, Utilization, etc. indicated the necessary maintenance strategy to normalize the process. After the root cause of shrink was found, improvements were implemented and the performance of the station was assessed again to confirm results

Scheduling of biological samples for DNA sequencing

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 95-97).In a DNA sequencing workflow, a biological sample has to pass through multiple process steps. Two consecutive steps are hydroshearing and library construction. Samples arrive randomly into the inventory and are to complete both processes before their due dates. The research project is to decide the optimal sequence of samples to go through these two processes subject to operational constraints. Two approaches, namely, heuristic and integer programming have been pursued in this thesis. A heuristic algorithm is proposed to solve the scheduling problem. A variant of the problem involving deterministic arrivals of samples is also considered for comparison purposes. Comparison tests between the two approaches are carried out to investigate the performance of the proposed heuristic for the original problem and its variant. Sensitivity analysis of the schedule to parameters of the problem is also conducted when using both approaches.by Yuwei Hu and Chin Soon Lim.S.M

Efficient Automated Planning with New Formulations

Problem solving usually strongly relies on how the problem is formulated. This fact also applies to automated planning, a key field in artificial intelligence research. Classical planning used to be dominated by STRIPS formulation, a simple model based on propositional logic. In the recently introduced SAS+ formulation, the multi-valued variables naturally depict certain invariants that are missed in STRIPS, make SAS+ have many favorable features. Because of its rich structural information SAS+ begins to attract lots of research interest. Existing works, however, are mostly limited to one single thing: to improve heuristic functions. This is in sharp contrast with the abundance of planning models and techniques in the field. On the other hand, although heuristic is a key part for search, its effectiveness is limited. Recent investigations have shown that even if we have almost perfect heuristics, the number of states to visit is still exponential. Therefore, there is a barrier between the nice features of SAS+ and its applications in planning algorithms. In this dissertation, we have recasted two major planning paradigms: state space search and planning as Satisfiability: SAT), with three major contributions. First, we have utilized SAS+ for a new hierarchical state space search model by taking advantage of the decomposable structure within SAS+. This algorithm can greatly reduce the time complexity for planning. Second, planning as Satisfiability is a major planning approach, but it is traditionally based on STRIPS. We have developed a new SAS+ based SAT encoding scheme: SASE) for planning. The state space modeled by SASE shows a decomposable structure with certain components independent to others, showing promising structure that STRIPS based encoding does not have. Third, the expressiveness of planning is important for real world scenarios, thus we have also extended the planning as SAT to temporally expressive planning and planning with action costs, two advanced features beyond classical planning. The resulting planner is competitive to state-of-the-art planners, in terms of both quality and performance. Overall, our work strongly suggests a shifting trend of planning from STRIPS to SAS+, and shows the power of formulating planning problems as Satisfiability. Given the important roles of both classical planning and temporal planning, our work will inspire new developments in other advanced planning problem domains

Supporting IT Service Fault Recovery with an Automated Planning Method

Despite advances in software and hardware technologies, faults are still inevitable in a highly-dependent, human-engineered and administrated IT environment. Given the critical role of IT services today, it is imperative that faults, having once occurred, have to be dealt with eciently and eeffectively to avoid or reduce the actual losses. Nevertheless, the complexities of current IT services, e.g., with regard to their scales, heterogeneity and highly dynamic infrastructures, make the recovery operation a challenging task for operators. Such complexities will eventually outgrow the human capability to manage them. Such diculty is augmented by the fact that there are few well-devised methods available to support fault recovery. To tackle this issue, this thesis aims at providing a computer-aided approach to assist operators with fault recovery planning and, consequently, to increase the eciency of recovery activities.We propose a generic framework based on the automated planning theory to generate plans for recoveries of IT services. At the heart of the framework is a planning component. Assisted by the other participants in the framework, the planning component aggregates the relevant information and computes recovery steps accordingly. The main idea behind the planning component is to sustain the planning operations with automated planning techniques, which is one of the research fields of articial intelligence. Provided with a general planning model, we show theoretically that the service fault recovery problem can be indeed solved by automated planning techniques. The relationship between a planning problem and a fault recovery problem is shown by means of reduction between these problems. After an extensive investigation, we choose a planning paradigm that based on Hierarchical Task Networks (HTN) as the guideline for the design of our main planning algorithm called H2MAP. To sustain the operation of the planner, a set of components revolving around the planning component is provided. These components are responsible for tasks such as translation between dierent knowledge formats, persistent storage of planning knowledge and communication with external systems. To ensure extendibility in our design, we apply dierent design patterns for the components. We sketch and discuss the technical aspects of implementations of the core components. Finally, as proof of the concept, the framework is instantiated to two distinguishing application scenarios