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.

The classical AI planning problems in the mirror of Horn linear logic: semantics, expressibility, complexity

2001 ◽  
Vol 11 (6) ◽  
pp. 689-716 ◽  
Author(s):  
MAX KANOVICH ◽  
JACQUELINE VAUZEILLES
Keyword(s):  
Linear Logic ◽  
Planning Problem ◽  
Ai Planning ◽  
Logical System ◽  
Robot System ◽  
Descriptive Complexity ◽  
Robot Systems ◽  
Finite Set ◽  
Planning Problems ◽  
Finite Domains

We introduce Horn linear logic as a comprehensive logical system capable of handling the typical AI problem of making a plan of the actions to be performed by a robot so that he could get into a set of final situations, if he started with a certain initial situation. Contrary to undecidability of propositional Horn linear logic, the planning problem is proved to be decidable for a reasonably wide class of natural robot systems.The planning problem is proved to be EXPTIME-complete for the robot systems that allow actions with non-deterministic effects. Fixing a finite signature, that is a finite set of predicates and their finite domains, we get a polynomial time procedure of making plans for the robot system over this signature.The planning complexity is reduced to PSPACE for the robot systems with only pure deterministic actions.As honest numerical parameters in our algorithms we invoke the length of description of a planning task ‘from W to Z˜’ and the Kolmogorov descriptive complexity of AxT, a set of possible actions.

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.

Chapter 19. Planning and SAT

2021 ◽  
Author(s):  
Jussi Rintanen
Keyword(s):  
Artificial Intelligence ◽  
Model Checking ◽  
Bounded Model Checking ◽  
Planning Problem ◽  
Ai Planning ◽  
Transition Systems ◽  
Computer Aided ◽  
Sat Solver ◽  
Main Ideas ◽  
Planning Problems

The planning problem in Artificial Intelligence was the first application of SAT to reasoning about transition systems and a direct precursor to the use of SAT in a number of other applications, including bounded model-checking in computer-aided verification. This chapter presents the main ideas about encoding goal reachability problems as a SAT problem, including parallel plans and different forms of constraints for speeding up SAT solving, as well as algorithms for solving the AI planning problem with a SAT solver. Finally, more general planning problems that require the use of QBF or other generalizations of SAT are discussed.

Operational Planning Using Mixed Integer Programming

1988 ◽  
Vol 64 (6) ◽  
pp. 485-488 ◽  
Author(s):  
H. Douglas Walker ◽  
Stephen W. Preiss
Keyword(s):  
Planning Process ◽  
Cost Savings ◽  
Mathematical Structure ◽  
Timber Harvesting ◽  
Operational Planning ◽  
Mixed Integer ◽  
Forest Planning ◽  
Planning Problem ◽  
Process Data ◽  
Planning Problems

A mathematical model was constructed and used to help plan five-year timber harvesting and delivery activities from an industrially managed public forest in Ontario. Harvest systems, harvest levels, and wood flows from compartments within the forest to various mills and delivery points were scheduled to minimize costs. The mathematical structure of the model may suggest applications to related forest planning problems. The model was useful in addressing the planning problem, and model results were used within the company's planning process. Data accuracy problems precluded assessing definitively the expected cost savings resulting from model use.

A direct approach to solving trajectory planning problems using genetic algorithms with dynamics considerations in complex environments

Robotica ◽  
2014 ◽  
Vol 33 (3) ◽  
pp. 669-683 ◽  
Author(s):  
Fares J. Abu-Dakka ◽  
Francisco J. Valero ◽  
Jose Luis Suñer ◽  
Vicente Mata
Keyword(s):  
Trajectory Planning ◽  
Direct Method ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Computational Time ◽  
Planning Problem ◽  
Complex Environments ◽  
Operational Parameters ◽  
Algorithm Efficiency ◽  
Planning Problems

SUMMARYThis paper presents a new genetic algorithm methodology to solve the trajectory planning problem. This methodology can obtain smooth trajectories for industrial robots in complex environments using a direct method. The algorithm simultaneously creates a collision-free trajectory between initial and final configurations as the robot moves. The presented method deals with the uncertainties associated with the unknown kinematic properties of intermediate via points since they are generated as the algorithm evolves looking for the solution. Additionally, the objective of this algorithm is to minimize the trajectory time, which guides the robot motion. The method has been applied successfully to the PUMA 560 robotic system. Four operational parameters (execution time, computational time, end-effector distance traveled, and significant points distance traveled) have been computed to study and analyze the algorithm efficiency. The experimental results show that the proposed optimization algorithm for the trajectory planning problem of an industrial robot is feasible.

scholarly journals A comprehensive approach for managing feasible solutions in production planning by an interacting network of Zero-Suppressed Binary Decision Diagrams

2015 ◽  
Vol 2 (2) ◽  
pp. 105-112 ◽  
Author(s):  
Keita Takahashi ◽  
Masahiko Onosato ◽  
Fumiki Tanaka
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Binary Decision Diagrams ◽  
Solution Space ◽  
Product Lifecycle Management ◽  
Planning Problem ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Feasible Solutions ◽  
Planning Problems

Abstract Product Lifecycle Management (PLM) ranges from design concepts of products to disposal. In this paper, we focus on the production planning phase in PLM, which is related to process planning and production scheduling and so on. In this study, key decisions for the creation of production plans are defined as production-planning attributes. Production-planning attributes correlate complexly in production-planning problems. Traditionally, the production-planning problem splits sub-problems based on experiences, because of the complexity. In addition, the orders in which to solve each sub-problem are determined by priorities between sub-problems. However, such approaches make solution space over-restricted and make it difficult to find a better solution. We have proposed a representation of combinations of alternatives in production-planning attributes by using Zero-Suppressed Binary Decision Diagrams. The ZDD represents only feasible combinations of alternatives that satisfy constraints in the production planning. Moreover, we have developed a solution search method that solves production-planning problems with ZDDs. In this paper, we propose an approach for managing solution candidates by ZDDs' network for addressing larger production-planning problems. The network can be created by linkages of ZDDs that express constraints in individual sub-problems and between sub-problems. The benefit of this approach is that it represents solution space, satisfying whole constraints in the production planning. This case study shows that the validity of the proposed approach.

scholarly journals USEFULNESS OF THE CONSTRAINED PLANNING PROBLEM IN A MODEL OF MONEY

2008 ◽  
Vol 12 (4) ◽  
pp. 503-525 ◽  
Author(s):  
Joydeep Bhattacharya ◽  
Rajesh Singh
Keyword(s):  
Government Policy ◽  
Optimal Policy ◽  
Policy Choice ◽  
Planning Problem ◽  
Equilibrium Concept ◽  
Limited Communication ◽  
Monetary Economy ◽  
Optimal Policies ◽  
Set Up ◽  
Planning Problems

In this paper, we study a decentralized monetary economy with a specified set of markets, rules of trade, an equilibrium concept, and a restricted set of policies and derive a set of equilibrium (monetary) allocations generated by these policies. Next we set up a simpler constrained planning problem in which we restrict the planner to choose from a set that contains the set of equilibrium allocations in the decentralized economy. If there is a government policy that allows the decentralized economy to achieve the constrained planner's allocation, then it is the optimal policy choice. To illustrate the power of such analyses, we solve such planning problems in three monetary environments with limited communication. The upshot is that solving constrained planning problems is potentially an extremely “efficient” (easy and quick) way of deriving optimal policies for the corresponding decentralized economies.

Parameter self-adaptation in biped navigation employing nonuniform randomized footstep planner

Robotica ◽  
2010 ◽  
Vol 28 (6) ◽  
pp. 929-936 ◽  
Author(s):  
Zeyang Xia ◽  
Jing Xiong ◽  
Ken Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Planning Problem ◽  
Navigation Problem ◽  
Probability Threshold ◽  
Self Adaptation ◽  
The Relationship ◽  
Planning Problems ◽  
Key Parameter

SUMMARYIn our previous work, a random-sampling-based footstep planner has been proposed for global biped navigation. Goal-probability threshold (GPT) is the key parameter that controls the convergence rate of the goal-biased nonuniform sampling in the planner. In this paper, an approach to optimized GPT adaptation is explained by a benchmarking planning problem. We first construct a benchmarking model, in which the biped navigation problem is described in selected parameters, to study the relationship between these parameters and the optimized GPT. Then, a back-propagation (BP) neural network is employed to fit this relationship. With a trained BP neural network modular, the optimized GPT can be automatically generated according to the specifications of a planning problem. Compared with previous methods of manual and empirical tuning of GPT for individual planning problems, the proposed approach is self-adaptive. Numerical experiments verified the performance of the proposed approach and furthermore showed that planning with BP-generated GPTs is more stable. Besides the implementation in specific parameterized environments studied in this paper, we attempt to provide the frame of the proposed approach as a reference for footstep planning in other environments.

scholarly journals Multiquery Motion Planning in Uncertain Spaces: Incremental Adaptive Randomized Roadmaps

2019 ◽  
Vol 29 (4) ◽  
pp. 641-654
Author(s):  
Weria Khaksar ◽  
Md Zia Uddin ◽  
Jim Torresen
Keyword(s):  
Motion Planning ◽  
Minimum Distance ◽  
Planning Problem ◽  
Grid Structure ◽  
Discrete Time System ◽  
Environment Map ◽  
Sample Classification ◽  
Adjustment Strategy ◽  
Hybrid Sample ◽  
Planning Problems

Abstract Sampling-based motion planning is a powerful tool in solving the motion planning problem for a variety of different robotic platforms. As its application domains grow, more complicated planning problems arise that challenge the functionality of these planners. One of the main challenges in the implementation of a sampling-based planner is its weak performance when reacting to uncertainty in robot motion, obstacles motion, and sensing noise. In this paper, a multi-query sampling-based planner is presented based on the optimal probabilistic roadmaps algorithm that employs a hybrid sample classification and graph adjustment strategy to handle diverse types of planning uncertainty such as sensing noise, unknown static and dynamic obstacles and an inaccurate environment map in a discrete-time system. The proposed method starts by storing the collision-free generated samples in a matrix-grid structure. Using the resulting grid structure makes it computationally cheap to search and find samples in a specific region. As soon as the robot senses an obstacle during the execution of the initial plan, the occupied grid cells are detected, relevant samples are selected, and in-collision vertices are removed within the vision range of the robot. Furthermore, a second layer of nodes connected to the current direct neighbors are checked against collision, which gives the planner more time to react to uncertainty before getting too close to an obstacle. The simulation results for problems with various sources of uncertainty show a significant improvement compared with similar algorithms in terms of the failure rate, the processing time and the minimum distance from obstacles. The planner is also successfully implemented and tested on a TurtleBot in four different scenarios with uncertainty.

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