15 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 competition’s central role in shaping the landscape of ongoing
developments in evaluating planning systems
Machine Learning and Adaptation of Domain Models to Support Real Time Planning in Autonomous Systems:Month 6 Report
Enabling Autonomic Properties in Road Transport System
Current autonomic computing systems tend to rely
on reactive rather than deliberative reasoning, that is, they use a simpler form of reasoning over sets of de�fined rules in order to be able to work in real-time. However, technology in areas such as automated planning or constraints processing have been developing rapidly, so that now it may be possible to deploy deliberative reasoning to real-time applications. In this paper, we introduce the problem of self-management of a road traffi�c network as a temporal planning problem. We design a road traffic model, and use it with domain independent planners to consider the feasibility of introducing it into tra�ffic management applications
Towards Application of Automated Planning in Urban Traffic Control
Advanced urban traffic control systems are often based on feed-back algorithms. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible co-ordination in road (sub) networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. Therefore, we need self-managing systems that can plan and act effectively in order to restore an unexpected road traffic situations into the normal order. A significant step towards this is exploiting Automated Planning techniques which can reason about unforeseen situations in the road network and come up with plans (sequences of actions) achieving a desired traffic situation. In this paper, we introduce the problem of self-management of a road traffic network as a temporal planning problem in order to effectively navigate cars throughout a road network. We demonstrate the feasibility of such a concept and discuss our preliminary evaluation in order to identify strengths and weaknesses of our approach and point to some promising directions of future research
Generating Macro-operators by Exploiting Inner Entanglements
In Automated Planning, learning and exploiting additional
knowledge within a domain model, in order
to improve plan generation speed-up and increase
the scope of problems solved, has attracted much research.
Reformulation techniques such as those based
on macro-operators or entanglements are very promising
because they are to some extent domain model and
planning engine independent. This paper aims to exploit
recent work on inner entanglements, relations between
pairs of planning operators and predicates encapsulating
exclusivity of predicate ‘achievements‘ or ‘requirements’,
for generating macro-operators. We provide
a theoretical study resulting in a set of conditions
when planning operators in an inner entanglement relation
can be removed from a domain model and replaced
by a macro-operator without compromising solvability
of a given (class of) problem(s). The effectiveness of
our approach will be experimentally shown on a set
of well-known benchmark domains using several highperforming
planning engines
Linear Logic in Planning
Linear Logic is a powerful formalism used to manage a lot of problems with resources. Linear Logic can also be used to formalize Petri Nets and to solve simple planning problems (for example ‘Block World‘). Research goes ahead also in Linear Logic Programming, which means that we have tools, that can solve Linear Logic problems. In this paper I will show the possible connection between solving planning problems and Linear Logic Programming
Determining Linearity of Optimal Plans by Operator Schema Analysis
Analysing the structures of solution plans generated by
AI Planning engines is helpful in improving the generative
planning process, as well as shedding light in
the study of its theoretical foundations.We investigate a
specific property of solution plans, that we called linearity,
which refers to a situation where each action
achieves an atom (or atoms) for a directly following action,
or achieves goal atom(s). Similarly, linearity can
be defined for parallel plans where each action in a
set of actions executed at some time step, achieves either
goal atom(s) or atom(s) for some action executed
in the directly following time step. In this paper, we
present a general and problem-independent theoretical
framework focusing on the analysis of planning operator
schema, namely relations of achiever, clobberer and
independence, in order to determine whether solvable
planning problems using a given operator schema have
as solutions optimal (parallel) plans which are linear.
The findings presented in this paper deepen current theoretical
knowledge, provide helpful information to engineers
of new planning domain models, and suggest
new ways of improving the performance of state-of-theart
(optimal) planning engines
Planning and Acting with Non-Deterministic Events: Navigating between Safe States
Automated Planning addresses the problem of finding a sequence of actions, a plan, transforming the environment from its initial state to some goal state. In real-world environments, exogenous events might occur and might modify the environment without agent's consent. Besides disrupting agent's plan, events might hinder agent's pursuit towards its goals and even cause damage (e.g. destroying the robot).In this paper, we leverage the notion of Safe States in dynamic environments under presence of non-deterministic exogenous events that might eventually cause dead-ends (e.g. “damage” the agent) if the agent is not careful while executing its plan. We introduce a technique for generating plans that constrains the number of consecutive “unsafe” actions in a plan and a technique for generating “robust” plans that effectively evade event effects. Combination of both approaches plans and executes robust plans between safe states. We empirically show that such an approach effectively navigates the agent towards its goals in spite of presence of dead-ends