Semantic reasoning in service robots using expert systems

Savage, Jesús; Rosenblueth, David A.; Matamoros, Mauricio; Negrete, Marco; Contreras, Luis; Cruz, Javier; Martell, Reynaldo; Estrada, Hugo; Okada, Hiroyuki

doi:10.1016/j.robot.2019.01.007

Cited by 38 publications

(19 citation statements)

References 4 publications

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“…In the semantic parsing of robot-directed speech, rule-based systems and methods based on symbolic AI have been used for a long time [ Figure 1 (a)] (Fischer et al, 1996 ; Lauria et al, 2002 ; Ljunglöf, 2014 ; Packard, 2014 ; Savage et al, 2019 ). However, the development of rule-based semantic parsing systems requires significant labor, and such systems are usually not robust to noise, i.e., recognition errors are incurred.…”

Section: Introductionmentioning

confidence: 99%

Robust Understanding of Robot-Directed Speech Commands Using Sequence to Sequence With Noise Injection

et al. 2020

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This paper describes a new method that enables a service robot to understand spoken commands in a robust manner using off-the-shelf automatic speech recognition (ASR) systems and an encoder-decoder neural network with noise injection. In numerous instances, the understanding of spoken commands in the area of service robotics is modeled as a mapping of speech signals to a sequence of commands that can be understood and performed by a robot. In a conventional approach, speech signals are recognized, and semantic parsing is applied to infer the command sequence from the utterance. However, if errors occur during the process of speech recognition, a conventional semantic parsing method cannot be appropriately applied because most natural language processing methods do not recognize such errors. We propose the use of encoder-decoder neural networks, e.g., sequence to sequence, with noise injection. The noise is injected into phoneme sequences during the training phase of encoder-decoder neural network-based semantic parsing systems. We demonstrate that the use of neural networks with a noise injection can mitigate the negative effects of speech recognition errors in understanding robot-directed speech commands i.e., increase the performance of semantic parsing. We implemented the method and evaluated it using the commands given during a general purpose service robot (GPSR) task, such as a task applied in RoboCup@Home, which is a standard service robot competition for the testing of service robots. The results of the experiment show that the proposed method, namely, sequence to sequence with noise injection (Seq2Seq-NI), outperforms the baseline methods. In addition, Seq2Seq-NI enables a robot to understand a spoken command even when the speech recognition by an off-the-shelf ASR system contains recognition errors. Moreover, in this paper we describe an experiment conducted to evaluate the influence of the injected noise and provide a discussion of the results.

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Section: Introductionmentioning

confidence: 99%

Robust Understanding of Robot-Directed Speech Commands Using Sequence to Sequence With Noise Injection

et al. 2020

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“…On the other hand, semantic refers to the meaning of each word and sentence. The semantic analysis of natural language content captures the real meaning; it processes the logical structure of sentences to find the similarities between words and understand the topic discussed in the sentences [12].…”

Section: B Natural Language Processingmentioning

confidence: 99%

Generating and Analyzing Chatbot Responses using Natural Language Processing

Aleedy¹,

Shaiba²,

Bezbradica³

2019

IJACSA

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Customer support has become one of the most important communication tools used by companies to provide before and after-sale services to customers. This includes communicating through websites, phones, and social media platforms such as Twitter. The connection becomes much faster and easier with the support of today's technologies. In the field of customer service, companies use virtual agents (Chatbot) to provide customer assistance through desktop interfaces. In this research, the main focus will be on the automatic generation of conversation "Chat" between a computer and a human by developing an interactive artificial intelligent agent through the use of natural language processing and deep learning techniques such as Long Short-Term Memory, Gated Recurrent Units and Convolution Neural Network to predict a suitable and automatic response to customers' queries. Based on the nature of this project, we need to apply sequence-to-sequence learning, which means mapping a sequence of words representing the query to another sequence of words representing the response. Moreover, computational techniques for learning, understanding, and producing human language content are needed. In order to achieve this goal, this paper discusses efforts towards data preparation. Then, explain the model design, generate responses, and apply evaluation metrics such as Bilingual Evaluation Understudy and cosine similarity. The experimental results on the three models are very promising, especially with Long Short-Term Memory and Gated Recurrent Units. They are useful in responses to emotional queries and can provide general, meaningful responses suitable for customer query. LSTM has been chosen to be the final model because it gets the best results in all evaluation metrics.

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“…Research on service robots has received increasing attention in recent years [1][2][3]. In most scenarios, like nursing homes and offices, humans hope that robots can help them do many tasks, which include taking orders and serving drinks, welcoming and guiding guests, or just cleaning up.…”

Section: Introductionmentioning

confidence: 99%

“…Abukhalil et al [12] present a high-level configuration and task assignment software package that distributes algorithms on a swarm of robots, which allows them to operate in a swarm fashion. Savage et al [3] use a conceptual-dependency [13] interpreter extracts semantic role structures from the input sentence and planning with the open-source expert system CLIPS [14]. Puigbo et al [15] adopt Soar cognitive architecture [16] to support understanding and executing human-specified commands.…”

Section: Introductionmentioning

confidence: 99%

Semantic Task Planning for Service Robots in Open Worlds

2021

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This paper presents a planning system based on semantic reasoning for a general-purpose service robot, which is aimed at behaving more intelligently in domains that contain incomplete information, under-specified goals, and dynamic changes. First, Two kinds of data are generated by Natural Language Processing module from the speech: (i) action frames and their relationships; (ii) the modifier used to indicate some property or characteristic of a variable in the action frame. Next, the task’s goals are generated from these action frames and modifiers. These goals are represented as AI symbols, combining world state and domain knowledge, which are used to generate plans by an Answer Set Programming solver. Finally, the plan’s actions are executed one by one, and continuous sensing grounds useful information, which makes the robot use contingent knowledge to adapt to dynamic changes and faults. For each action in the plan, the planner gets its preconditions and effects from domain knowledge, so during the execution of the task, the environmental changes, especially those conflict with the actions, not only the action being performed but also the subsequent actions, can be detected and handled as early as possible. A series of case studies are used to evaluate the system and verify its ability to acquire knowledge through dialogue with users, solve problems with the acquired causal knowledge, and plan for complex tasks autonomously in the open world.

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Semantic reasoning in service robots using expert systems

Cited by 38 publications

References 4 publications

Robust Understanding of Robot-Directed Speech Commands Using Sequence to Sequence With Noise Injection

Robust Understanding of Robot-Directed Speech Commands Using Sequence to Sequence With Noise Injection

Generating and Analyzing Chatbot Responses using Natural Language Processing

Semantic Task Planning for Service Robots in Open Worlds

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