1,720,971 research outputs found
Symbolic Pattern Planning
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Temporal Numeric Planning with Patterns
Full text linkWe consider temporal numeric planning problems Π expressed in PDDL2.1, and show how it is possible to produce SMT formulas (i) whose models correspond to valid plans of Π, and (ii) which extends the recently proposed planning with patterns approach from the numeric to the temporal case. We prove the correctness and completeness of the approach and that it outperforms all the publicly available temporal planners on 10 domains with required concurrency
Symbolic Numeric Planning with Patterns
Full text linkIn this paper, we propose a novel approach for solving linear numeric planning problems, called Symbolic Pattern Planning. Given a planning problem Pi, a bound n and a pattern --defined as an arbitrary sequence of actions-- we encode the problem of finding a plan for Pi with bound n as a formula with fewer variables and/or clauses than the state-of-the-art rolled-up and relaxed-relaxed-exists encodings. More importantly, we prove that for any given bound, it is never the case that the latter two encodings allow finding a valid plan while ours does not. On the experimental side, we consider 6 other planning systems --including the ones which participated in this year's International Planning Competition (IPC)-- and we show that our planner Patty has remarkably good comparative performances on this year's IPC problems
Initial Condition Retrieving for Hybrid and Numeric Planning Problems
Full text linkReal-world applications of planning techniques often deal with dynamic and noisy environments, where sensor readings are often inaccurate, and the world's states can evolve in unexpected ways. This is particularly challenging for hybrid discrete-continuous planning approaches, where processes and events can be strongly affected by even slightly different initial conditions of the world, and planning tasks are notoriously difficult to cope with. In this paper, we introduce the Initial Condition Retrieving (ICR) problem to foster hybrid planning in real-world applications. Given a knowledge model of a planning task and a trace, solving the ICR problem allows identifying the space of all the initial conditions from which the provided plan is guaranteed to reach a goal state. We define three tasks: (i) retrieving any valid initial condition, (ii) fixing only some desired initial values and retrieving a complete initial condition that fills in the unassigned values, or (iii) retrieving the closest achievable initial condition to a fully specified one from which the goal cannot be reached. Experiments on well-known hybrid planning domains demonstrate the efficacy of our approach in solving such tasks. Moreover, given that our approach can be applied to numeric planning without any change, we extend our analysis to numeric domains, where we obtain positive results
A Framework for Risk-Aware Routing of Connected Vehicles via Artificial Intelligence
Full text linkThe advent of Connected Autonomous Vehicles can enable the use of Artificial Intelligence (AI) techniques to support urban traffic controllers in extending their control capabilities with the ability to distribute vehicles in a urban region. Vehicles can communicate their destination, and receive an optimised route by traffic controllers. While the benefits of traffic routing are clear, it is also clear that re-routing has the potential to increase risks for vehicles’ and passengers’ safety due to environmental or urban factors. There is however a lack of work in the area of risk-aware routing. To fill the above-mentioned gap, we introduce a framework to incorporate risk-awareness in the vehicle routing process. The proposed framework provides a principled structure to define and characterise different classes of risk that can arise in a region, allowing to take them into account when generating routes. We show how this framework can be implemented, and we provide an empirical analysis of its performance on two European urban areas.<br/
Optimising Dynamic Traffic Distribution for Urban Networks with Answer Set Programming
Full text linkAnswer set programming (ASP) has demonstrated its potential as an effective tool for concisely representing and reasoning about real-world problems. In this paper, we present an application in which ASP has been successfully used in the context of dynamic traffic distribution for urban networks, within a more general framework devised for solving such a real-world problem. In particular, ASP has been employed for the computation of the "optimal"routes for all the vehicles in the network. We also provide an empirical analysis of the performance of the whole framework, and of its part in which ASP is employed, on two European urban areas, which shows the viability of the framework and the contribution ASP can give.</p
Taming Discretised PDDL+ through Multiple Discretisations
No full textThe PDDL+ formalism allows the use of planning techniques in applications that require the ability to perform hybrid discrete-continuous reasoning. PDDL+ problems are notoriously challenging to tackle, and to reason upon them a well-established approach is discretisation. Existing systems rely on a single discretisation delta or, at most, two: a simulation delta to model the dynamics of the environment, and a planning delta, that is used to specify when decisions can be taken. However, there exist cases where this rigid schema is not ideal, for instance when agents with very different speeds need to cooperate or interact in a shared environment, and a more flexible approach that can accommodate more deltas is necessary. To address the needs of this class of hybrid planning problems, in this paper we introduce a reformulation approach that allows the encapsulation of different levels of discretisation in PDDL+ models, hence allowing any domain-independent planning engine to reap the benefits. Further, we provide the community with a new set of benchmarks that highlights the limits of fixed discretisation
Rescheduling rehabilitation sessions with answer set programming
Full text linkThe rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several requirements and optimizations, e.g. patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular as a consequence of the COVID-19 pandemic that significantly increased rehabilitation’s needs. The problem has been recently successfully solved via a two-phase solution based on answer set programming (ASP). In this paper, we focus on the problem of rescheduling the rehabilitation sessions, which comes into play when the original schedule cannot be implemented, for reasons that involve the unavailability of operators and/or the absence of patients. We provide rescheduling solutions based on ASP for both phases, considering different scenarios. Results of experiments performed on real benchmarks, provided by ICS Maugeri, show that also the rescheduling problem can be solved in a satisfactory way. Finally, we present a web application that supports the usage of our solution
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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