symbolic planning

Extracted Attributes

• Field

  Artificial Intelligence

• Key tools

  Frames

• Early goal

  Artificial general intelligence (AGI)

• Also known as

  Logic-based artificial intelligence

• Current trend

  Combination with subsymbolic approaches (neural networks)

• Core principles

  High-level symbolic (human-readable) representations of problems, logic, and search

• Major challenges

  Brittleness in handling out-of-domain problems

• Dominant paradigm period

  Mid-1950s to Mid-1990s

• Purpose of current trend

  Addressing common-sense reasoning and other complex issues

Timeline

• Development of Logic Theorist, an early success in symbolic AI. (Source: Wikipedia)

  1956-01-01

• Development of Samuel's Checkers Playing Program, another early success. (Source: Wikipedia)

  1959-01-01

• Start of the second boom in AI research, marked by the rise of expert systems (lasting until 1986). (Source: Wikipedia)

  1969-01-01

• Start of the second AI Winter, caused by difficulties in knowledge acquisition, maintaining large knowledge bases, and brittleness (lasting until 2011). (Source: Wikipedia)

  1988-01-01

• An increasing number of AI researchers began calling for combining symbolic and neural network approaches. (Source: Wikipedia)

  2020-01-01

Symbolic artificial intelligence

In artificial intelligence, symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. Symbolic AI was the dominant paradigm of AI research from the mid-1950s until the mid-1990s. Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the ultimate goal of their field. An early boom, with early successes such as the Logic Theorist and Samuel's Checkers Playing Program, led to unrealistic expectations and promises and was followed by the first AI Winter as funding dried up. A second boom (1969–1986) occurred with the rise of expert systems, their promise of capturing corporate expertise, and an enthusiastic corporate embrace. That boom, and some early successes, e.g., with XCON at DEC, was followed again by later disappointment. Problems with difficulties in knowledge acquisition, maintaining large knowledge bases, and brittleness in handling out-of-domain problems arose. Another, second, AI Winter (1988–2011) followed. Subsequently, AI researchers focused on addressing underlying problems in handling uncertainty and in knowledge acquisition. Uncertainty was addressed with formal methods such as hidden Markov models, Bayesian reasoning, and statistical relational learning. Symbolic machine learning addressed the knowledge acquisition problem with contributions including Version Space, Valiant's PAC learning, Quinlan's ID3 decision-tree learning, case-based learning, and inductive logic programming to learn relations. Neural networks, a subsymbolic approach, had been pursued from early days and reemerged strongly in 2012. Early examples are Rosenblatt's perceptron learning work, the backpropagation work of Rumelhart, Hinton and Williams, and work in convolutional neural networks by LeCun et al. in 1989. However, neural networks were not viewed as successful until about 2012: "Until Big Data became commonplace, the general consensus in the Al community was that the so-called neural-network approach was hopeless. Systems just didn't work that well, compared to other methods. ... A revolution came in 2012, when a number of people, including a team of researchers working with Hinton, worked out a way to use the power of GPUs to enormously increase the power of neural networks." Over the next several years, deep learning had spectacular success in handling vision, speech recognition, speech synthesis, image generation, and machine translation. However, since 2020, as inherent difficulties with bias, explanation, comprehensibility, and robustness became more apparent with deep learning approaches; an increasing number of AI researchers have called for combining the best of both the symbolic and neural network approaches and addressing areas that both approaches have difficulty with, such as common-sense reasoning.

Web Search Results

• [PDF] Symbolic Planning and Code Generation for Grounded Dialogue

  White et al., 2021; Chiu et al., 2022). Symbolic planning allows SPC to explic-itly and efficiently optimize for task success while taking advantage of task-specific properties. [...] Our approach to grounded reference games sep-arates symbolic reasoning from language, allowing explicit steering. Our system, Symbolic Planning and Code-generation (SPC), breaks down a turn into three procedures: reading, planning, and writ-ing. Reading and writing convert from language to symbols and vice versa, while planning reasons in purely symbolic space. [...] 9 Conclusion We present Symbolic Planning and Code-generation (SPC), a method that approaches grounded task-oriented dialogue by separating sym-bolic reasoning from language. Our approach uses an LLM to generate executable code functions which represent the meaning of utterances, map-ping from language to symbolic actions. We then symbolically track task progress using Bayesian reasoning, and explicitly plan the best actions to take next using an information gain objective. De-spite using

• Symbolic AI in Robotics: A Comprehensive Guide - SmythOS

  The power of symbolic planning becomes evident in robotic assembly operations. When a robot needs to assemble components, it uses logic programming to break down complex tasks into manageable sequences. The system considers factors like spatial relationships, physical constraints, and assembly order while maintaining safety protocols and operational efficiency. [...] The integration of symbolic planning with other AI techniques has led to more robust robotic systems. For example, manufacturing robots can now learn from experience while maintaining the predictability and safety guarantees that symbolic systems provide. This combination of learning and logic-based planning has proven effective in dynamic manufacturing environments where adaptability is crucial. [...] ## Make an agent faster than a cup of coffee ## Applications of Symbolic AI in Robotic Planning Symbolic AI has transformed robotic planning by offering structured, rule-based approaches to complex decision-making processes. Through logical programming and symbolic reasoning, robots can generate action sequences to achieve specific goals while adapting to changing environments.

• Learning Type-Generalized Actions for Symbolic Planning - arXiv

  | | | --- | | Comments: | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2023 | | Subjects: | Artificial Intelligence (cs.AI); Robotics (cs.RO) | | Cite as: | arXiv:2308.04867 [cs.AI] | | | (or arXiv:2308.04867v2 [cs.AI] for this version) | | | Focus to learn more arXiv-issued DOI via DataCite | | Related DOI: | Focus to learn more DOI(s) linking to related resources | ## Submission history ## Access Paper: ### References & Citations [...] Cornell University arxiv logo Help | Advanced Search arXiv logo Cornell University Logo ## quick links # Computer Science > Artificial Intelligence # Title:Learning Type-Generalized Actions for Symbolic Planning [...] ## BibTeX formatted citation ### Bookmark BibSonomy logo Reddit logo # Bibliographic and Citation Tools # Code, Data and Media Associated with this Article # Demos # Recommenders and Search Tools # arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

• The Power of AI Symbolic Systems Approach - LinkedIn

  The symbolic systems approach and AI planning work great for applications that have a limited number of patterns; for example, a program that helps you complete your tax return. The IRS provides a limited number of forms and a collection of rules for reporting tax-relevant data. Combine the forms and instructions with the capability to crunch numbers and some heuristic reasoning, and you have a tax program that can step you through the process. With heuristic reasoning, you can limit the number

• Symbolic artificial intelligence - Wikipedia

  and it developed applications such as knowledge-based systems (in particular, expert systems), symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems. [...] planning, deduction, and deliberative thinking. In this view, deep learning best models the first kind of thinking while symbolic reasoning best models the second kind and both are needed. [...] strategy to another as conditions – such as goals or times – changed. BB1 has been applied in multiple domains: construction site planning, intelligent tutoring systems, and real-time patient monitoring.