Hierarchical task network-based emergency task planning with incomplete information, concurrency and uncertain duration

AbstractWe consider task planning for long-living intelligent agents situated in dynamic environments. Specifically, we address the problem of incomplete knowledge of the world due to the addition of new objects with unknown action models. We propose a multilayered agent architecture that uses meta-reasoning to control hierarchical task planning and situated learning, monitor expectations generated by a plan against world observations, forms goals and rewards for the situated reinforcement learner, and learns the missing planning knowledge relevant to the new objects. We use occupancy grids as a low-level representation for the high-level expectations to capture changes in the physical world due to the additional objects, and provide a similarity method for detecting discrepancies between the expectations and the observations at run-time; the meta-reasoner uses these discrepancies to formulate goals and rewards for the learner, and the learned policies are added to the hierarchical task network plan library for future re-use. We describe our experiments in the Minecraft and Gazebo microworlds to demonstrate the efficacy of the architecture and the technique for learning. We test our approach against an ablated reinforcement learning (RL) version, and our results indicate this form of expectation enhances the learning curve for RL while being more generic than propositional representations.

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The value of two-person zero-sum repeated games with incomplete information and uncertain duration

International Journal of Game Theory ◽

10.1007/s00182-011-0281-y ◽

2011 ◽

Vol 41 (1) ◽

pp. 195-207 ◽

Cited By ~ 4

Author(s):

Abraham Neyman

Keyword(s):

Incomplete Information ◽

Repeated Games ◽

Uncertain Duration ◽

Games With Incomplete Information ◽

Zero Sum

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Integrating task planning, execution and monitoring for a domestic service robot

it - Information Technology ◽

10.1515/itit-2014-1064 ◽

2015 ◽

Vol 57 (2) ◽

Author(s):

Elizaveta Shpieva ◽

Iman Awaad

Keyword(s):

Real World ◽

Procedural Knowledge ◽

Planning Process ◽

State Machine ◽

Domestic Service ◽

Service Robot ◽

Artificial Agents ◽

State Machines ◽

Task Planning ◽

Hierarchical Task Network

AbstractEmbodied artificial agents operating in dynamic, real-world environments need architectures that support the special requirements that exist for them. Architectures are not always designed from scratch and the system then implemented all at once, but rather, a step-wise integration of components is often made to increase functionality. In order to increase flexibility and robustness, a task planner was integrated into an existing architecture and the planning process was coupled with the pre-existing execution and the basic monitoring processes. This involved the conversion of monolithic SMACH scenario scripts (state-machine execution scripts) into modular states that can be called dynamically based on the plan that was generated by the planning process. The procedural knowledge encoded in such state machines was used to model the planning domain for two RoboCup@Home scenarios on a Care-O-Bot 3 robot. This was done for the JSHOP2 hierarchical task network (HTN) planner. A component which iterates through a generated plan and calls the appropriate SMACH states was implemented, thus enabling the scenarios. Crucially, individual monitoring actions which enable the robot to monitor the execution of the actions were designed and included, thus providing additional robustness.

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Hierarchical Task Planning for Multiarm Robot with Multiconstraint

Mathematical Problems in Engineering ◽

10.1155/2016/2508304 ◽

2016 ◽

Vol 2016 ◽

pp. 1-17 ◽

Cited By ~ 3

Author(s):

Yifan Wang ◽

Hanxu Sun ◽

Gang Chen ◽

Qingxuan Jia ◽

Boyang Yu

Keyword(s):

Profile Analysis ◽

Planning Method ◽

Space Robot ◽

Simple Path ◽

Complex Task ◽

Task Planning ◽

Space Robots ◽

Hierarchical Task Network ◽

Mission Profile ◽

And Task

Multiarm systems become the trends of space robots, for the on-orbit servicing missions are becoming more complex and various. A hierarchical task planning method with multiconstraint for multiarm space robot is presented in this paper. The process of task planning is separated into two hierarchies: mission profile analysis and task node planning. In mission profile analysis, several kinds of primitive tasks and operators are defined. Then, a complex task can be decomposed into a sequence of primitive tasks by using hierarchical task network (HTN) with those primitive tasks and operators. In task node planning,A⁎algorithm is improved to adapt the continuous motion of manipulator. Then, some of the primitive tasks which cannot be executed directly because of constraints are further decomposed into several task nodes by using improvedA⁎algorithm. Finally, manipulators execute the task by moving from one node to another with a simple path plan algorithm. The feasibility and effectiveness of the proposed task planning method are verified by simulation.

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Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

Mathematical Problems in Engineering ◽

10.1155/2019/4259649 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14

Author(s):

Jie Zhang ◽

Gang Wang ◽

Yafei Song ◽

Fangzheng Zhao ◽

Siyuan Wang

Keyword(s):

Control Structure ◽

Resource Scheduling ◽

Command And Control ◽

Coordination Mechanism ◽

Task Planning ◽

Good Convergence ◽

Hierarchical Task Network ◽

Planning Algorithm ◽

Low Efficiency ◽

And Control

For task planning of the command and control structure, the existing algorithms exhibit low efficiency and poor replanning quality under abnormal conditions. Given the requirements of the current accusation architecture, a distributed command and control structure model is built in this paper based on multiagents, which exploits the superiority of multiagents in achieving complex tasks. The concept of MultiAgent-HTN is proposed based on the framework. The original hierarchical task network planning algorithm is optimized, the multiagent collaboration framework is redefined, and the coordination mechanism of local conflict is developed. With the classical resource scheduling problem as the experimental background, the proposed algorithm compared with the classical HTN algorithm is drawn. According to the experimental results, the proposed algorithm exhibits higher quality and higher efficiency than the existing algorithm and the space anomaly is significant in the course of processing. The planning is more efficient and the time is more complicated and superior in solving the same problem, and the algorithm exhibits good convergence and adaptability. In the conclusion, it is proved that the distributed command and control structure proposed in this paper exhibits high practicability in relevant fields and can solve the problem of distributed command and control structure in a multiagent scenario.

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