# Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives

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## Abstract

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## 1. Introduction

## 2. State of the Art: 60 Years in a Nutshell

#### 2.1. The Premise: Computational Logic

#### 2.2. Knowledge Representation

#### 2.3. Reasoning Approaches and Techniques

#### 2.4. Verification and Model-Checking

## 3. Logic-Based AI: Application Areas

#### 3.1. Ai Foundations

#### 3.1.1. Formalization and Verification of Computational Systems

#### 3.1.2. Cognitive Agents and Intelligent Systems

#### 3.2. AI for Society

#### 3.2.1. Healthcare and Wellbeing

#### 3.2.2. Law and Governance

#### 3.2.3. Education

#### 3.3. AI for Business: Automation and Robotics

#### 3.3.1. Planning and Task Allocation

#### 3.3.2. Robotics and Control

## 4. Perspective and Future Trends

#### 4.1. Integration of Symbolic and Sub-Symbolic AI

- sub-symbolic AI is opaque, meaning that human beings struggle in understanding the functioning and behavior of sub-symbolically intelligent systems; instead, symbolic AI is more transparent, as it is both human- and machine-interpretable at the same time
- sub-symbolic AI can improve itself automatically by consuming data, but it is difficult to extend and re-use outside the contexts it was designed for; conversely, symbolic AI is flexible and extensible, but requires humans to manually provide symbolic knowledge
- sub-symbolic AI is adequate for fuzzy problems where some (minimal) degree of error or uncertainty can be tolerated; whereas symbolic AI calls for precise data and queries provided by human beings, yet provides exact, crisp results as its outcome.

#### 4.1.1. Techniques and Approaches: Hybrid Models for Intelligent Systems

#### 4.1.2. Application Scenarios: Explainable, Responsible, and Ethical AI

#### 4.2. Relational Learning

#### Inductive Logic Programming

#### 4.3. Constraint (Logic) Programming

- will (C)LP ever reach full declarativeness? In other words, will it ever be possible to write CLP programs containing custom, user-defined domains and constraints?
- can sub-symbolic AI play a role in the development of more efficient or more expressive CLP solvers?

#### 4.4. Argumentation

#### Argumentation Mining

#### 4.5. Coordination and Self-Organization

#### 4.6. Education

#### 4.7. Declarative Languages

- what is favoring adoption of non-logic declarative technologies?
- what is preventing a wider adoption of logic-based declarative technologies in these areas?
- can computational logic and logic programming contribute in overcoming the current shortcomings of non-logic declarative technologies?

## 5. Discussion and Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**A graphical view of symbolic and sub-symbolic approaches highlighting the role of logic-based technologies along with their main classification.

**Figure 2.**Logic-based technologies application areas with respect to the main AI categories—namely AI Foundations, AI for Society, and AI for Business. Intentionally, the picture only illustrates the AI areas that are closely related to logic.

**Figure 3.**Perspective and future trends for logic-based technologies in symbolic AI. The directional signs illustrate the main research directions currently aiming to answer the AI’s hottest needs, sketched in the CoW (cloud of words) on the right. Words there come from the PDF of the papers discussed in Section 4—Perspectives and Future Trends.

**Table 1.**Sorts of logic per application area. Acronym and abbreviation key: FOL: First-Order Logic; DL: Description Logic; BDI: Belief–Desire–Intention (Logic); TL: Temporal Logic; FL: Fuzzy Logic; PL: Probabilistic Logic; DR: Defeasible Reasoning; CLP: Constraint Logic Programming.

FOL | DL | BDI | TL | FL | PL | DR | CLP | |
---|---|---|---|---|---|---|---|---|

Formalization & Verification | ✓ | ✓ | ||||||

Cognitive Agents | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||

Healthcare & Wellbeing | ✓ | ✓ | ✓ | |||||

Law & Governance | ✓ | ✓ | ||||||

Education | ✓ | ✓ | ✓ | |||||

Planning & Task Allocation | ✓ | ✓ | ✓ | ✓ | ||||

Robotics & Control | ✓ | ✓ | ✓ | ✓ |

**Table 2.**Sorts of logic per market segment. Acronym and abbreviation key: FOL: First-Order Logic; DL: Description Logic; BDI: Belief-Desire-Intention (Logic); TL: Temporal Logic; PL: Probabilistic Logic; DR: Defeasible Reasoning; CLP: Constraint Logic Programming.

FOL | DL | BDI | TL | FL | PL | DR | CLP | |
---|---|---|---|---|---|---|---|---|

Aerospace | [54,55] | [56] | ||||||

Analytics | [57] | [58] | ||||||

Bioinformatics | [57] | [12,17] | [59,60] | [61] | [56] | |||

BPM | [57] | [17] | ||||||

Constructions | [56] | |||||||

Critical systems | [62,63] | |||||||

CPS | [17] | [58,64] | [65] | [66,67] | [61] | |||

Cybersecurity | [68,69] | [61] | ||||||

Databases | [12] | [70,71] | ||||||

Decision support | [72,73] | [74] | ||||||

Energy | [24] | [58] | [65] | [75] | [61] | [56] | ||

Finance | [24] | [76,77] | [56] | |||||

Government & Legal | [78] | |||||||

Hardware | [24] | [79,80] | [56] | |||||

Healthcare | [58,81] | [65] | [82,83] | |||||

Information retrieval | [12] | [84,85] | ||||||

Logistic | [57] | [24] | [58,64] | [65] | [56] | |||

Manufacturing | [58,64] | [65] | [86,87] | |||||

Mechanics | [56] | |||||||

Mobile applications | [88,89] | |||||||

Railways | [63] | [90,91] | ||||||

Telecommunications | [57] | [92,93] | [61] | [56] | ||||

Transports | [58] | [94,95] | ||||||

Web services | [12,17] | [96,97] | [98,99] |

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Calegari, R.; Ciatto, G.; Denti, E.; Omicini, A.
Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives. *Information* **2020**, *11*, 167.
https://doi.org/10.3390/info11030167

**AMA Style**

Calegari R, Ciatto G, Denti E, Omicini A.
Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives. *Information*. 2020; 11(3):167.
https://doi.org/10.3390/info11030167

**Chicago/Turabian Style**

Calegari, Roberta, Giovanni Ciatto, Enrico Denti, and Andrea Omicini.
2020. "Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives" *Information* 11, no. 3: 167.
https://doi.org/10.3390/info11030167