1,516 research outputs found
Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures
Case-Based Planning (CBP) provides a way of scaling up domain-independent
planning to solve large problems in complex domains. It replaces the detailed
and lengthy search for a solution with the retrieval and adaptation of previous
planning experiences. In general, CBP has been demonstrated to improve
performance over generative (from-scratch) planning. However, the performance
improvements it provides are dependent on adequate judgements as to problem
similarity. In particular, although CBP may substantially reduce planning
effort overall, it is subject to a mis-retrieval problem. The success of CBP
depends on these retrieval errors being relatively rare. This paper describes
the design and implementation of a replay framework for the case-based planner
DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating
explanation-based learning techniques that allow it to explain and learn from
the retrieval failures it encounters. These techniques are used to refine
judgements about case similarity in response to feedback when a wrong decision
has been made. The same failure analysis is used in building the case library,
through the addition of repairing cases. Large problems are split and stored as
single goal subproblems. Multi-goal problems are stored only when these smaller
cases fail to be merged into a full solution. An empirical evaluation of this
approach demonstrates the advantage of learning from experienced retrieval
failure.Comment: See http://www.jair.org/ for any accompanying file
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
A Domain-Independent Algorithm for Plan Adaptation
The paradigms of transformational planning, case-based planning, and plan
debugging all involve a process known as plan adaptation - modifying or
repairing an old plan so it solves a new problem. In this paper we provide a
domain-independent algorithm for plan adaptation, demonstrate that it is sound,
complete, and systematic, and compare it to other adaptation algorithms in the
literature. Our approach is based on a view of planning as searching a graph of
partial plans. Generative planning starts at the graph's root and moves from
node to node using plan-refinement operators. In planning by adaptation, a
library plan - an arbitrary node in the plan graph - is the starting point for
the search, and the plan-adaptation algorithm can apply both the same
refinement operators available to a generative planner and can also retract
constraints and steps from the plan. Our algorithm's completeness ensures that
the adaptation algorithm will eventually search the entire graph and its
systematicity ensures that it will do so without redundantly searching any
parts of the graph.Comment: See http://www.jair.org/ for any accompanying file
Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has been applied successfully to many
robotic applications. However, the large number of trials needed for training
is a key issue. Most of existing techniques developed to improve training
efficiency (e.g. imitation) target on general tasks rather than being tailored
for robot applications, which have their specific context to benefit from. We
propose a novel framework, Assisted Reinforcement Learning, where a classical
controller (e.g. a PID controller) is used as an alternative, switchable policy
to speed up training of DRL for local planning and navigation problems. The
core idea is that the simple control law allows the robot to rapidly learn
sensible primitives, like driving in a straight line, instead of random
exploration. As the actor network becomes more advanced, it can then take over
to perform more complex actions, like obstacle avoidance. Eventually, the
simple controller can be discarded entirely. We show that not only does this
technique train faster, it also is less sensitive to the structure of the DRL
network and consistently outperforms a standard Deep Deterministic Policy
Gradient network. We demonstrate the results in both simulation and real-world
experiments.Comment: Published in ICRA2018. The code is now available at
https://github.com/xie9187/AsDDP
Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation
A generic computer simulation for manipulator systems (ROBSIM) was implemented and the specific technologies necessary to increase the role of automation in various missions were developed. The specific items developed are: (1) capability for definition of a manipulator system consisting of multiple arms, load objects, and an environment; (2) capability for kinematic analysis, requirements analysis, and response simulation of manipulator motion; (3) postprocessing options such as graphic replay of simulated motion and manipulator parameter plotting; (4) investigation and simulation of various control methods including manual force/torque and active compliances control; (5) evaluation and implementation of three obstacle avoidance methods; (6) video simulation and edge detection; and (7) software simulation validation
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Transformational maintenance by reuse of design histories
This thesis provides theory and procedures for modifying software artifacts implemented by a formal transformation process. Installing modifications requires knowing not only what transformations were applied (a derivation history) to construct the artifact, but also why the application sequence ensures that the artifact meets its specification. The derivation history and the justification are collectively called a design history. A Design Maintenance System (DMS), when provided with a formal change called a maintenance delta, revises a design history to guide construction of a new artifact. A DMS can be used to integrate a stream of deltas into a history, providing implementations as a side effect, leading to an incremental-evolution model for software construction.We provide a broadly applicable formal model of transformation systems in which specifications are performance predicates, subsuming the functional specifications which are traditional for transformation systems. Such performance predicates provide vocabulary used in the design history to describe the effect of applying sets of transformations.A nonprocedural, performance-goal-oriented Transformation Control Language (TCL) is defined to control navigation of the design space for a transformation system. Recording the execution of a TCL metaprogram directly provides a design history.A complete classification of, and representation for, the set of possible maintenance deltas is given in terms of the inputs defined by the transformation system model. Such deltas include not only specification changes, but also changes to implementation support technologies. Delta integration procedures for revising derivation histories given functional or support technology deltas are provided, based on rearranging the order of transformations in the design space. Building on these operations, integration procedures that revise the design history for each type of delta are described. An agenda-oriented TCL execution process dovetails smoothly with the integration procedures.Our DMS is compared to a number of other maintenance systems. By using an explicit delta and verified commutativity, our DMS often reuses transformations correctly when others fail
Goal-Conditioned Predictive Coding as an Implicit Planner for Offline Reinforcement Learning
Recent work has demonstrated the effectiveness of formulating decision making
as a supervised learning problem on offline-collected trajectories. However,
the benefits of performing sequence modeling on trajectory data is not yet
clear. In this work we investigate if sequence modeling has the capability to
condense trajectories into useful representations that can contribute to policy
learning. To achieve this, we adopt a two-stage framework that first summarizes
trajectories with sequence modeling techniques, and then employs these
representations to learn a policy along with a desired goal. This design allows
many existing supervised offline RL methods to be considered as specific
instances of our framework. Within this framework, we introduce
Goal-Conditioned Predicitve Coding (GCPC), an approach that brings powerful
trajectory representations and leads to performant policies. We conduct
extensive empirical evaluations on AntMaze, FrankaKitchen and Locomotion
environments, and observe that sequence modeling has a significant impact on
some decision making tasks. In addition, we demonstrate that GCPC learns a
goal-conditioned latent representation about the future, which serves as an
"implicit planner", and enables competitive performance on all three
benchmarks
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