Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

Abstract

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

Logic programming for deliberative robotic task planning

“…MLNs are based on probabilistic weighted logic statements. In Al-Moadhen et al (2016) task planning for manipulation and navigation scenarios is implemented querying a MLN with the STRIPSbased Metric-FF planner (Hoffmann and Nebel 2001); in Lisca et al (2015) a robot for chemical experiment automation is proposed querying probabilistic action cores (PRACs) (Nyga and Beetz 2012) based on MLNs. A mention is also deserved by LP M LN Wang 2016, 2018) to reason on MLNs using the answer set semantics under the action language BC+ (Babb and Lee 2015) for transition systems, though the actual application in robotic scenarios is still part of ongoing research.…”

Novel Hybrid Algorithms for a Single Machine Scheduling Problem With an Overtime Constraint

“…Fifth, the solution approaches that are frequently used for large-scale problems, are the heuristic and metaheuristic algorithms with their modifications or hybrids. These approaches are applied to other sophisticated combinatorial problems as well (Al-Moadhen et al, 2016;Chiadamrong & Tangchaisuk, 2021;Hewahi, 2015;Rerkjirattikal & Olapiriyakul, 2019;Rerkjirattikal et al, 2020a;Rerkjirattikal et al, 2020b;Srizongkhram et al, 2020;Zhang & Guo, 2011). Finally, for large-scale problems, the effectiveness of the proposed algorithms is evaluated by comparing their solutions with the existing algorithms or the known bounds from the exact algorithm.…”

A Human-Aware Method to Plan Complex Cooperative and Autonomous Tasks using Behavior Trees

“…To solve the task planning problem, it is necessary to exploit models that allow taking into account all the possible robot actions and reactions in accomplishing a set or sequence of tasks. This can be achieved by taking advantage of heuristics [17] and deterministic [18] methods. Specifically, in [17], the authors proposed a method that can decompose a causal graph of a translated planning task into a sequence of tasks and to plan with a heuristic system.…”

“…Specifically, in [17], the authors proposed a method that can decompose a causal graph of a translated planning task into a sequence of tasks and to plan with a heuristic system. In [18], the presented approach generates plans including probabilistic values, which are obtained from Markov Logic Networks (MLN), a learning statistical relational model. The problem of blending decision making with task execution has been widely tackled with methods like Decision Trees (DTs) [19] and Hierarchical Finite State Machines (HFSMs) [20].…”