12,146 research outputs found
On Different Strategies for Eliminating Redundant Actions from Plans
Satisficing planning engines are often able to generate plans in a reasonable time, however, plans are often far from optimal. Such plans often contain a high number of redundant actions, that are actions, which can be removed without affecting the validity of the plans. Existing approaches for determining and eliminating redundant actions work in polynomial time, however, do not guarantee eliminating the "best" set of redundant actions, since such a problem is NP-complete. We introduce an approach which encodes the problem of determining the "best" set of redundant actions (i.e. having the maximum total-cost) as a weighted MaxSAT problem. Moreover, we adapt the existing polynomial technique which greedily tries to eliminate an action and its dependants from the plan in order to eliminate more expensive redundant actions. The proposed approaches are empirically compared to existing approaches on plans generated by state-of-the-art planning engines on standard planning benchmark
Exploiting Block Deordering for Improving Planners Efficiency
Capturing and exploiting structural knowledge of
planning problems has shown to be a successful
strategy for making the planning process more ef-
ficient. Plans can be decomposed into its constituent
coherent subplans, called blocks, that encapsulate
some effects and preconditions, reducing
interference and thus allowing more deordering
of plans. According to the nature of blocks, they
can be straightforwardly transformed into useful
macro-operators (shortly, âmacrosâ). Macros are
well known and widely studied kind of structural
knowledge because they can be easily encoded in
the domain model and thus exploited by standard
planning engines.
In this paper, we introduce a method, called
BLOMA, that learns domain-specific macros from
plans, decomposed into âmacro-blocksâ which are
extensions of blocks, utilising structural knowledge
they capture. In contrast to existing macro learning
techniques, macro-blocks are often able to capture
high-level activities that form a basis for useful
longer macros (i.e. those consisting of more original
operators). Our method is evaluated by using
the IPC benchmarks with state-of-the-art planning
engines, and shows considerable improvement in
many cases
Efficient Open World Reasoning for Planning
We consider the problem of reasoning and planning with incomplete knowledge
and deterministic actions. We introduce a knowledge representation scheme
called PSIPLAN that can effectively represent incompleteness of an agent's
knowledge while allowing for sound, complete and tractable entailment in
domains where the set of all objects is either unknown or infinite. We present
a procedure for state update resulting from taking an action in PSIPLAN that is
correct, complete and has only polynomial complexity. State update is performed
without considering the set of all possible worlds corresponding to the
knowledge state. As a result, planning with PSIPLAN is done without direct
manipulation of possible worlds. PSIPLAN representation underlies the PSIPOP
planning algorithm that handles quantified goals with or without exceptions
that no other domain independent planner has been shown to achieve. PSIPLAN has
been implemented in Common Lisp and used in an application on planning in a
collaborative interface.Comment: 39 pages, 13 figures. to appear in Logical Methods in Computer
Scienc
The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning
This paper presents GRT, a domain-independent heuristic planning system for
STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase,
it estimates the distance between each fact and the goals of the problem, in a
backward direction. Then, in the search phase, these estimates are used in
order to further estimate the distance between each intermediate state and the
goals, guiding so the search process in a forward direction and on a best-first
basis. The paper presents the benefits from the adoption of opposite directions
between the preprocessing and the search phases, discusses some difficulties
that arise in the pre-processing phase and introduces techniques to cope with
them. Moreover, it presents several methods of improving the efficiency of the
heuristic, by enriching the representation and by reducing the size of the
problem. Finally, a method of overcoming local optimal states, based on domain
axioms, is proposed. According to it, difficult problems are decomposed into
easier sub-problems that have to be solved sequentially. The performance
results from various domains, including those of the recent planning
competitions, show that GRT is among the fastest planners
First principles planning in BDI systems
BDI (Belief, Desire, Intention) agent systems are very powerful, but they lack the ability to incorporate planning. There has been some previous work to incorporate planning within such systems. However, this has either focussed on producing low-level plan sequences, losing much of the domain knowledge inherent in BDI systems, or has been limited to HTN (Hierarchical Task Network) planning, which cannot find plans other than those specified by the programmer. In this work, we incorporate classical planning into a BDI agent, but in a way that respects and makes use of the procedural domain knowledge available, by producing abstract plans that can be executed using such knowledge. In doing so, we recognize an intrinsic tension between striving for abstract plans and, at the same time, ensuring that unnecessary actions, unrelated to the specific goal to be achieved, are avoided. We explore this tension, by first characterizing the set of 'ideal' abstract plans that are non-redundant while maximally abstract, and then developing a more limited but feasible account in which an abstract plan is 'specialized' into a new abstract plan that is non-redundant and preserves abstraction as much as possible. We describe an algorithm to compute such a plan specialization, as well as algorithms for the production of a valid high level plan, by deriving abstract planning operators from the BDI program
Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning
[EN] The conciliation of multiple single-disease guidelines for comorbid patients entails solving potential clinical interactions, discovering synergies in the diagnosis and the recommendations, and managing clinical equipoise situations. Personalized conciliation of multiple guidelines considering additionally patient preferences brings some further difficulties. Recently, several works have explored distinct techniques to come up with an automated process for the conciliation of clinical guidelines for comorbid patients but very little attention has been put in integrating the patient preferences into this process.
In this work, a Multi-Agent Planning (MAP) framework that extends previous work on single-disease temporal Hierarchical Task Networks (HTN) is proposed for the automated conciliation of clinical guidelines with patient-centered preferences. Each agent encapsulates a single-disease Computer Interpretable Guideline (CIG) formalized as an HTN domain and conciliates the decision procedures that encode the clinical recommendations of its CIG with the decision procedures of the other agents' CIGs. During conciliation, drug-related interactions, scheduling constraints as well as redundant actions and multiple support interactions are solved by an automated planning process. Moreover, the simultaneous application of the patient preferences in multiple diseases may potentially bring about contradictory clinical decisions and more interactions. As a final step, the most adequate personalized treatment plan according to the patient preferences is selected by a Multi-Criteria Decision Making (MCDM) process. The MAP approach is tested on a case study that builds upon a simplified representation of two real clinical guidelines for Diabetes Mellitus and Arterial Hypertension.This work has been partially supported by Spanish Government Projects MINECO TIN2014-55637-C2-2-R and TIN2015-71618-R.FernĂĄndez-Olivares, J.; Onaindia De La Rivaherrera, E.; Castillo Vidal, L.; JordĂĄn, J.; CĂłzar, J. (2019). Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning. Artificial Intelligence in Medicine. 96:167-186. https://doi.org/10.1016/j.artmed.2018.11.003S1671869
Improving the efficiency of the Pre-Optimization Plan Techniques
Automated planning is an important research area of Artificial Intelligence (AI). In classical planning, which is a sub-area of automated planning, attention is given to âagileâ planning, i.e., solving planning problems as quickly as possible regardless of the quality of solution plans. Obtaining solutions quickly is important for real-time applications as well as in situations of imminent danger. Post-planning optimisation techniques for improving the quality of solution plans are a good option for improving poor quality plans. Since such techniques are run as post-processing, this avoids situations where there is a risk of not having solution plans in time. This thesis focuses on an important sub-area of post-planning optimisation; that is, on identifying and removing redundant actions from solution plans. In particular, this study extends the existing Action Elimination and Greedy Action Elimination algorithms by introduce two approaches to improve their efficiency. The AE and GAE algorithms are thereby developed into the UAIAE and UGAIAE systems respectively. The key to our approaches is based on optimise the process while keeping the same elimination powerâ (identifying and removing the same number of redundant actions). First approach improves the algorithms by considering situations where inverse actions are redundant, while the other identifies a subset of actions that cannot be present in any redundant actions set. This subset is named justified unique actions. The studyâs approach to identifying this subset has been motivated by a promising heuristic approach called âlandmarksâ, which are facts or actions that cannot be eliminated to achieve the goal.
The approaches in this study have been empirically evaluated using several benchmark domains, as well as several planning engines that participated in the Agile track of the International Planning Competition 2014. In addition, they have been evaluated against state-of-the-art optimal and satisficing planners, as well as they are evaluated against a plan repair technique.
The methods of AE family can be understood as polynomial methods that improve the quality of a plan by removing redundant actions, or as tools to complement more sophisticated plan optimisation techniques
Risk-specific search for risk-defusing operators
According to the concept of âactive risk-defusing behaviorâ, decision makers in risky situations look for additional actions that reduce risk and allow them to favor the more risky alternative. Our study demonstrates that risk-defusing behavior depends on the type of risk (normal, medium, catastrophic or global) as well as on the domain (health, economy or ecology). In total, 12 scenarios (four risk types from three risk domains each) were constructed. Using the interview techniques of active information search and thinking-aloud, 120 interviews about decision-making processes with these scenarios were conducted. They showed that the active search for different risk-defusing operators depends on the type of risk, but even more on the domain of the scenario. Results suggest a need for further research about a typology of risk situations in which, besides formal classification criteria, content issues are also explored
A Model of Redundant Information in Dialogue: The Role of Resource Bounds (Dissertation Proposal)
This document is a proposal of research intended to complete a Ph.D. in Computer Science. The overall goal of the proposed work is to demonstrate a connection between agents as limited reasoners and the use of informationally redundant utterances in problem-solving dialogues. This document describes some long range objectives and some preliminary results toward this goal. Comments from readers on the proposed work would be most welcome
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