scholarly journals Replanning in Domains with Partial Information and Sensing Actions

2012 ◽  
Vol 45 ◽  
pp. 565-600 ◽  
Author(s):  
R. I. Brafman ◽  
G. Shani
Keyword(s):  
Original Problem ◽  
Partial Information ◽  
The State ◽  
Belief State ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Online Planning ◽  
Sensing Actions ◽  
Planning Problems ◽  
Better Than

Replanning via determinization is a recent, popular approach for online planning in MDPs. In this paper we adapt this idea to classical, non-stochastic domains with partial information and sensing actions, presenting a new planner: SDR (Sample, Determinize, Replan). At each step we generate a solution plan to a classical planning problem induced by the original problem. We execute this plan as long as it is safe to do so. When this is no longer the case, we replan. The classical planning problem we generate is based on the translation-based approach for conformant planning introduced by Palacios and Geffner. The state of the classical planning problem generated in this approach captures the belief state of the agent in the original problem. Unfortunately, when this method is applied to planning problems with sensing, it yields a non-deterministic planning problem that is typically very large. Our main contribution is the introduction of state sampling techniques for overcoming these two problems. In addition, we introduce a novel, lazy, regression-based method for querying the agent's belief state during run-time. We provide a comprehensive experimental evaluation of the planner, showing that it scales better than the state-of-the-art CLG planner on existing benchmark problems, but also highlighting its weaknesses with new domains. We also discuss its theoretical guarantees.

scholarly journals Compact Tree Encodings for Planning as QBF

2018 ◽  
Vol 21 (62) ◽  
pp. 103-113
Author(s):  
Olivier Gasquet ◽  
Dominique Longin ◽  
Fr´ed´eric Maris ◽  
Pierre R´egnier ◽  
Ma¨el Valais
Keyword(s):  
Artificial Intelligence ◽  
Execution Time ◽  
Boolean Formula ◽  
Benchmark Problems ◽  
Sat Solvers ◽  
Quantified Boolean Formula ◽  
And Performance ◽  
Near Future ◽  
Planning Problems ◽  
Better Than

Considerable improvements in the technology and performance of SAT solvers has made their use possible for the resolution of various problems in artificial intelligence, and among them that of generating plans. Recently, promising Quantified Boolean Formula (QBF) solvers have been developed and we may expect that in a near future they become as efficient as SAT solvers. So, it is interesting to use QBF language that allows us to produce more compact encodings. We present in this article a translation from STRIPS planning problems into quantified propositional formulas. We introduce two new Compact Tree Encodings: CTE-EFA based on Explanatory frame axioms, and CTE-OPEN based on causal links. Then we compare both of them to CTE-NOOP based on No-op Actions proposed in [Cashmore et al. 2012]. In terms of execution time over benchmark problems, CTE-EFA and CTE-OPEN always performed better than CTE-NOOP.

Fast Strong Planning for FOND Problems with Multi-Root Directed Acyclic Graphs

2014 ◽  
Vol 23 (06) ◽  
pp. 1460028 ◽  
Author(s):  
Andres Calderon Jaramillo ◽  
Jicheng Fu ◽  
Vincent Ng ◽  
Farokh B. Bastani ◽  
I-Ling Yen
Keyword(s):  
State Of The Art ◽  
Solution Space ◽  
Strong Solutions ◽  
Search Space ◽  
Directed Acyclic Graphs ◽  
The State ◽  
Benchmark Problems ◽  
Acyclic Graphs ◽  
Planning Problems ◽  
Improve Planning

Recently, the state-of-the-art AI planners have significantly improved planning efficiency on Fully Observable Nondeterministic planning (FOND) problems with strong cyclic solutions. These strong cyclic solutions are guaranteed to achieve the goal if they terminate, implying that there is a possibility that they may run into indefinite loops. In contrast, strong solutions are guaranteed to achieve the goal, but few planners can effectively handle FOND problems with strong solutions. In this study, we aim to address this difficult, yet under-investigated class of planning problems: FOND planning problems with strong solutions. We present a planner that employs a new data structure, MRDAG (multi-root directed acyclic graph), to define how the solution space should be expanded. Based on the characteristics of MRDAG, we develop heuristics to ensure planning towards the relevant search direction and design optimizations to prune the search space to further improve planning efficiency. We perform extensive experiments to evaluate MRDAG, the heuristics, and the optimizations for pruning the search space. Experimental results show that our strong algorithm achieves impressive performance on a variety of benchmark problems: on average it runs more than three orders of magnitude faster than the state-of-the-art planners, MBP and Gamer, while demonstrating significantly better scalability.

scholarly journals Multi-Agent Planning with Baseline Regret Minimization

2017 ◽  
Author(s):  
Feng Wu ◽  
Shlomo Zilberstein ◽  
Xiaoping Chen
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Benchmark Problems ◽  
Minimization Algorithm ◽  
Generation Algorithm ◽  
Regret Minimization ◽  
Multi Agent ◽  
Planning Problems ◽  
Multi Agent Planning ◽  
Better Than

We propose a novel baseline regret minimization algorithm for multi-agent planning problems modeled as finite-horizon decentralized POMDPs. It guarantees to produce a policy that is provably better than or at least equivalent to the baseline policy. We also propose an iterative belief generation algorithm to effectively and efficiently minimize the baseline regret, which only requires necessary iterations to converge to the policy with minimum baseline regret. Experimental results on common benchmark problems confirm its advantage comparing to the state-of-the-art approaches.

scholarly journals Contrastive Explanations of Plans through Model Restrictions

2021 ◽  
Vol 72 ◽  
pp. 533-612
Author(s):  
Benjamin Krarup ◽  
Senka Krivic ◽  
Daniele Magazzeni ◽  
Derek Long ◽  
Michael Cashmore ◽  
...  
Keyword(s):  
Restricted Problem ◽  
Original Problem ◽  
User Study ◽  
Planning Problem ◽  
Ai Planning ◽  
Exploration Process ◽  
Iterative Model ◽  
Contrastive Explanation ◽  
Original Plan ◽  
Planning Problems

In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user’s expectation. We frame Explainable AI Planning as an iterative plan exploration process, in which the user asks a succession of contrastive questions that lead to the generation and solution of hypothetical planning problems that are restrictions of the original problem. The object of the exploration is for the user to understand the constraints that govern the original plan and, ultimately, to arrive at a satisfactory plan. We present the results of a user study that demonstrates that when users ask questions about plans, those questions are usually contrastive, i.e. “why A rather than B?”. We use the data from this study to construct a taxonomy of user questions that often arise during plan exploration. Our approach to iterative plan exploration is a process of successive model restriction. Each contrastive user question imposes a set of constraints on the planning problem, leading to the construction of a new hypothetical planning problem as a restriction of the original. Solving this restricted problem results in a plan that can be compared with the original plan, admitting a contrastive explanation. We formally define model-based compilations in PDDL2.1 for each type of constraint derived from a contrastive user question in the taxonomy, and empirically evaluate the compilations in terms of computational complexity. The compilations were implemented as part of an explanation framework supporting iterative model restriction. We demonstrate its benefits in a second user study.

scholarly journals Compact Tree Encodings for Planning as QBF

2018 ◽  
Vol 21 (62) ◽  
pp. 103 ◽  
Author(s):  
Olivier Gasquet
Keyword(s):  
Artificial Intelligence ◽  
Execution Time ◽  
Boolean Formula ◽  
Benchmark Problems ◽  
Sat Solvers ◽  
Quantified Boolean Formula ◽  
And Performance ◽  
Near Future ◽  
Planning Problems ◽  
Better Than

Considerable improvements in the technology and performance of SAT solvers has made their use possible for the resolution of various problems in artificial intelligence, and among them that of generating plans. Recently, promising Quantified Boolean Formula (QBF) solvers have been developed and we may expect that in a near future they become as efficient as SAT solvers. So, it is interesting to use QBF language that allows us to produce more compact encodings. We present in this article a translation from STRIPS planning problems into quantified propositional formulas. We introduce two new Compact Tree Encodings: CTE-EFA based on Explanatory frame axioms, and CTE-OPEN based on causal links. Then we compare both of them to CTE-NOOP based on No-op Actions proposed in [Cashmore et al. 2012]. In terms of execution time over benchmark problems, CTE-EFA and CTE-OPEN always performed better than CTE-NOOP.

scholarly journals REINFORCED ENCODING FOR PLANNING AS SAT

2015 ◽  
Vol 2 (2) ◽  
pp. 1-7
Author(s):  
Tomáš Balyo ◽  
Roman Barták ◽  
Otakar Trunda
Keyword(s):  
Automated Planning ◽  
The Other ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Encoding Scheme ◽  
International Planning ◽  
Planning Problems

Solving planning problems via translation to satisfiability (SAT) is one of the most successful approaches to automated planning. We propose a new encoding scheme, called Reinforced Encoding, which encodes a planning problem represented in the SAS+ formalism into SAT. The Reinforced Encoding is a combination of the transition-based SASE encoding with the classical propositional encoding. In our experiments we compare our new encoding to other known SAS+ based encodings. The results indicate, that he Reinforced encoding performs well on the benchmark problems of the 2011 International Planning Competition and can outperform all the other known encodings for several domains.

A Better Solution to the General Problem of Creation

2017 ◽  
Vol 9 (1) ◽  
pp. 147-162
Author(s):  
Jeremy W. Skrzypek
Keyword(s):  
Recent Work ◽  
General Problem ◽  
The State ◽  
State Of Affairs ◽  
Better Than

It is often suggested that, since the state of affairs in which God creates a good universe is better than the state of affairs in which He creates nothing, a perfectly good God would have to create that good universe. Making use of recent work by Christine Korgaard on the relational nature of the good, I argue that the state of affairs in which God creates is actually not better, due to the fact that it is not better for anyone or anything in particular. Hence, even a perfectly good God would not be compelled to create a good universe.

Some Effects of Discriminable Goal-Box Conditions on the Learning of a Successive Discrimination

1967 ◽  
Vol 19 (4) ◽  
pp. 319-326 ◽  
Author(s):  
M. H. Sheldon
Keyword(s):  
The State ◽  
Successive Discrimination ◽  
Better Than ◽  
Trial Performance

In an attempt to confirm and extend a previous result, rats were trained on two tasks where a signal delivered at the start of each trial indicated which of two paths through a maze would be rewarded. In Experiment I both paths led to the same goal-box, and it was found that performance was better when the state of the goal-box was different on trials with each of the two signals. In Experiment II the two paths led to spatially separated goal-boxes. It was found that when the states of the two goal-boxes were discriminably different but the state of each of them remained the same from trial to trial, performance was better than when their states varied irregularly. It is suggested that these results have interesting implications for theories of behaviour.

Trajectory Planning for Concrete Pump Truck Based on Intelligent Hill Climbing and Genetic Algorithm

2011 ◽  
Vol 127 ◽  
pp. 360-367 ◽  
Author(s):  
Xiao Dong Kang ◽  
Gang Huang ◽  
Xian Li Cao ◽  
Xiang Zhou
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Planning ◽  
Performance Comparison ◽  
Hill Climbing ◽  
Planning Problem ◽  
Online Planning ◽  
And Performance ◽  
Concrete Pump Truck ◽  
Concrete Pump ◽  
Constrained Conditions

This paper takes the five –link concrete pump boom as the research object, and transforms its trajectory planning issue into a multi-object optimization problem. Using intelligent hill climbing algorithm and genetic algorithm, and integrating them closely to ensure real-time online planning for the pump truck effectively, and make the planned motion trajectory for the boom is global optimized under particular constrained conditions. Simulation and performance comparison experiments show that this hybrid algorithm is practical and effective, which offers a new approach for the trajectory planning problem of concrete pump truck.

scholarly journals Data-driven planning via imitation learning

2018 ◽  
Vol 37 (13-14) ◽  
pp. 1632-1672 ◽  
Author(s):  
Sanjiban Choudhury ◽  
Mohak Bhardwaj ◽  
Sankalp Arora ◽  
Ashish Kapoor ◽  
Gireeja Ranade ◽  
...  
Keyword(s):  
Partial Information ◽  
State Of The Art ◽  
Imitation Learning ◽  
Data Driven ◽  
Sequential Decision ◽  
Efficient Manner ◽  
World State ◽  
Performance Guarantees ◽  
The World ◽  
Planning Problems

Robot planning is the process of selecting a sequence of actions that optimize for a task=specific objective. For instance, the objective for a navigation task would be to find collision-free paths, whereas the objective for an exploration task would be to map unknown areas. The optimal solutions to such tasks are heavily influenced by the implicit structure in the environment, i.e. the configuration of objects in the world. State-of-the-art planning approaches, however, do not exploit this structure, thereby expending valuable effort searching the action space instead of focusing on potentially good actions. In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until that time. We formulate this as a problem of sequential decision making under uncertainty where at a given iteration a planning policy must map the state of the search to a planning action. Unfortunately, the training process for such partial-information-based policies is slow to converge and susceptible to poor local minima. Our key insight is that if we could fully observe the underlying world map, we would easily be able to disambiguate between good and bad actions. We hence present a novel data-driven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle: an oracle that at train time has full knowledge about the world map and can compute optimal decisions. We leverage the fact that for planning problems, such oracles can be efficiently computed and derive performance guarantees for the learnt policy. We examine two important domains that rely on partial-information-based policies: informative path planning and search-based motion planning. We validate the approach on a spectrum of environments for both problem domains, including experiments on a real UAV, and show that the learnt policy consistently outperforms state-of-the-art algorithms. Our framework is able to train policies that achieve up to [Formula: see text] more reward than state-of-the art information-gathering heuristics and a [Formula: see text] speedup as compared with A* on search-based planning problems. Our approach paves the way forward for applying data-driven techniques to other such problem domains under the umbrella of robot planning.

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