100,117 research outputs found
The 2014 International Planning Competition: Progress and Trends
We review the 2014 International Planning Competition (IPC-2014), the eighth
in a series of competitions starting in 1998. IPC-2014 was held in three separate
parts to assess state-of-the-art in three prominent areas of planning research: the
deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic
part (IPPC). Each part evaluated planning systems in ways that pushed the edge of
existing planner performance by introducing new challenges, novel tasks, or both.
The competition surpassed again the number of competitors than its predecessor,
highlighting the competitionās central role in shaping the landscape of ongoing
developments in evaluating planning systems
Planning Through Stochastic Local Search and Temporal Action Graphs in LPG
We present some techniques for planning in domains specified with the recent
standard language PDDL2.1, supporting 'durative actions' and numerical
quantities. These techniques are implemented in LPG, a domain-independent
planner that took part in the 3rd International Planning Competition (IPC). LPG
is an incremental, any time system producing multi-criteria quality plans. The
core of the system is based on a stochastic local search method and on a
graph-based representation called 'Temporal Action Graphs' (TA-graphs). This
paper focuses on temporal planning, introducing TA-graphs and proposing some
techniques to guide the search in LPG using this representation. The
experimental results of the 3rd IPC, as well as further results presented in
this paper, show that our techniques can be very effective. Often LPG
outperforms all other fully-automated planners of the 3rd IPC in terms of speed
to derive a solution, or quality of the solutions that can be produced
The International planning competition series and empirical evaluation of AI planning systems
In this paper we consider the role of the International Planning Competition series in the evaluation of planners, both directly through the events themselves, and indirectly through the creation of resources and infrastructure. We also consider the problem of evaluation based on data collected both in the competitions and otherwise and examine some of the issues that arise in attempting to formulate and test hypotheses around the data
Hi-Val: Iterative Learning of Hierarchical Value Functions for Policy Generation
Task decomposition is effective in manifold applications where the global complexity of a problem makes planning and decision-making too demanding. This is true, for example, in high-dimensional robotics domains, where (1) unpredictabilities and modeling limitations typically prevent the manual specification of robust behaviors, and (2) learning an action policy is challenging due to the curse of dimensionality. In this work, we borrow the concept of Hierarchical Task Networks (HTNs) to decompose the learning procedure, and we exploit Upper Confidence Tree (UCT) search to introduce HOP, a novel iterative algorithm for hierarchical optimistic planning with learned value functions. To obtain better generalization and generate policies, HOP simultaneously learns and uses action values. These are used to formalize constraints within the search space and to reduce the dimensionality of the problem. We evaluate our algorithm both on a fetching task using a simulated 7-DOF KUKA light weight arm and, on a pick and delivery task with a Pioneer robot
Business and Information Technology Alignment Measurement -- a recent Literature Review
Since technology has been involved in the business context, Business and
Information Technology Alignment (BITA) has been one of the main concerns of IT
and Business executives and directors due to its importance to overall company
performance, especially today in the age of digital transformation. Several
models and frameworks have been developed for BITA implementation and for
measuring their level of success, each one with a different approach to this
desired state. The BITA measurement is one of the main decision-making tools in
the strategic domain of companies. In general, the classical-internal alignment
is the most measured domain and the external environment evolution alignment is
the least measured. This literature review aims to characterize and analyze
current research on BITA measurement with a comprehensive view of the works
published over the last 15 years to identify potential gaps and future areas of
research in the field.Comment: 12 pages, Preprint version, BIS 2018 International Workshops, Berlin,
Germany, July 18 to 20, 2018, Revised Paper
Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning
Case-Based planning can fruitfully exploit knowledge
gained by solving a large number of problems, storing
the corresponding solutions in a plan library and reusing
them for solving similar planning problems in the future.
Case-based planning is extremely effective when
similar reuse candidates can be efficiently chosen.
In this paper, we study an innovative technique based
on planning problem features for efficiently retrieving
solved planning problems (and relative plans) from
large plan libraries. A problem feature is a characteristic
of the instance that can be automatically derived from
the problem specification, domain and search space
analyses, and different problem encodings.
Since the use of existing planning features are not always
able to effectively distinguish between problems
within the same planning domain, we introduce a new
class of features.
An experimental analysis in this paper shows that our
features-based retrieval approach can significantly improve
the performance of a state-of-the-art case-based
planning system
Plan stability: replanning versus plan repair
The ultimate objective in planning is to construct plans for execution. However, when a plan is executed in a real environment it can encounter differences between the expected and actual context of execution. These differences can manifest as divergences between the expected and observed states of the world, or as a change in the goals to be achieved by the plan. In both cases, the old plan must be replaced with a new one. In replacing the plan an important consideration is plan stability. We compare two alternative strategies for achieving the {em stable} repair of a plan: one is simply to replan from scratch and the other is to adapt the existing plan to the new context. We present arguments to support the claim that plan stability is a valuable property. We then propose an implementation, based on LPG, of a plan repair strategy that adapts a plan to its new context. We demonstrate empirically that our plan repair strategy achieves more stability than replanning and can produce repaired plans more efficiently than replanning
- ā¦