32 research outputs found
An Adaptive Approach for Planning in Dynamic Environments
Planning in a dynamic environment is a
complex task that requires several issues to be
investigated in order to manage the associated
search complexity. In this paper, an adaptive
behavior that integrates planning with learning
is presented. The former is performed adopting a
hierarchical approach, interleaved with
execution. The latter, devised to identify new
abstract operators, adopts a chunking technique
on successful plans. Integration between
planning and learning is also promoted by an
agent architecture explicitly designed for
supporting abstraction
FLECS: Planning with a Flexible Commitment Strategy
There has been evidence that least-commitment planners can efficiently handle
planning problems that involve difficult goal interactions. This evidence has
led to the common belief that delayed-commitment is the "best" possible
planning strategy. However, we recently found evidence that eager-commitment
planners can handle a variety of planning problems more efficiently, in
particular those with difficult operator choices. Resigned to the futility of
trying to find a universally successful planning strategy, we devised a planner
that can be used to study which domains and problems are best for which
planning strategies. In this article we introduce this new planning algorithm,
FLECS, which uses a FLExible Commitment Strategy with respect to plan-step
orderings. It is able to use any strategy from delayed-commitment to
eager-commitment. The combination of delayed and eager operator-ordering
commitments allows FLECS to take advantage of the benefits of explicitly using
a simulated execution state and reasoning about planning constraints. FLECS can
vary its commitment strategy across different problems and domains, and also
during the course of a single planning problem. FLECS represents a novel
contribution to planning in that it explicitly provides the choice of which
commitment strategy to use while planning. FLECS provides a framework to
investigate the mapping from planning domains and problems to efficient
planning strategies.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
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
2Planning for Contingencies: A Decision-based Approach
A fundamental assumption made by classical AI planners is that there is no
uncertainty in the world: the planner has full knowledge of the conditions
under which the plan will be executed and the outcome of every action is fully
predictable. These planners cannot therefore construct contingency plans, i.e.,
plans in which different actions are performed in different circumstances. In
this paper we discuss some issues that arise in the representation and
construction of contingency plans and describe Cassandra, a partial-order
contingency planner. Cassandra uses explicit decision-steps that enable the
agent executing the plan to decide which plan branch to follow. The
decision-steps in a plan result in subgoals to acquire knowledge, which are
planned for in the same way as any other subgoals. Cassandra thus distinguishes
the process of gathering information from the process of making decisions. The
explicit representation of decisions in Cassandra allows a coherent approach to
the problems of contingent planning, and provides a solid base for extensions
such as the use of different decision-making procedures.Comment: See http://www.jair.org/ for any accompanying file
An Abstract Framework for Non-Cooperative Multi-Agent Planning
[EN] In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents¿ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.This work was partially funded by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by Universitat Politecnica de Valencia (UPV) PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana Fondo Social Europeo.Jordán, J.; Bajo, J.; Botti, V.; Julian Inglada, VJ. (2019). An Abstract Framework for Non-Cooperative Multi-Agent Planning. Applied Sciences. 9(23):1-18. https://doi.org/10.3390/app9235180S118923De Weerdt, M., & Clement, B. (2009). Introduction to planning in multiagent systems. 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I-Rescue: A Coalition Based System to Support Disaster Relief Operations
The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.I-Rescue is a research programme that aims to develop knowledge-based tools for disaster relief domains. One important aspect of the I-Rescue development is to highlight the requirements regarding the collaborative
activities of planning and execution, considering a hierarchical structure of decision-making and a mixed initiative
style of interaction between users and systems. This paper discusses the design and implementation of I-Rescue and its use in a search and rescue domain where joint users, assisted by customised agents, are able to
perform complementary tasks
A BDI agent programming language with failure handling, declarative goals, and planning
Agents are an important technology that have the potential to take over contemporary methods for analysing, designing, and implementing complex software. The Belief- Desire-Intention (BDI) agent paradigm has proven to be one of the major approaches to intelligent agent systems, both in academia and in industry. Typical BDI agent-oriented programming languages rely on user-provided ''plan libraries'' to achieve goals, and online context sensitive subgoal selection and expansion. These allow for the development of systems that are extremely flexible and responsive to the environment, and as a result, well suited for complex applications with (soft) real-time reasoning and control requirements. Nonetheless, complex decision making that goes beyond, but is compatible with, run-time context-dependent plan selection is one of the most natural and important next steps within this technology. In this paper we develop a typical BDI-style agent-oriented programming language that enhances usual BDI programming style with three distinguished features: declarative goals, look-ahead planning, and failure handling. First, an account that mixes both procedural and declarative aspects of goals is necessary in order to reason about important properties of goals and to decouple plans from what these plans are meant to achieve. Second, lookahead deliberation about the effects of one choice of expansion over another is clearly desirable or even mandatory in many circumstances so as to guarantee goal achievability and to avoid undesired situations. Finally, a failure handling mechanism, suitably integrated with both declarative goals and planning, is required in order to model an adequate level of commitment to goals, as well as to be consistent with most real BDI implemented systems