Distributed Artificial Intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of

artificial intelligence Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machine A machine is a physical system using Power (physics), power to apply Force, forces and control Motion, moveme ...

research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of

multi-agent system A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework fo ...

Definition

Distributed Artificial Intelligence (DAI) is an approach to solving complex learning,

planning Planning is the process of thinking regarding the activities required to achieve a desired goal. Planning is based on foresight, the fundamental capacity for mental time travel. The evolution of forethought, the capacity to think ahead, is c ...

, and decision making problems. It is

embarrassingly parallel In parallel computing, an embarrassingly parallel workload or problem (also called embarrassingly parallelizable, perfectly parallel, delightfully parallel or pleasingly parallel) is one where little or no effort is needed to separate the problem ...

, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets. DAI systems consist of autonomous learning processing nodes ( agents), that are distributed, often at a very large scale. DAI nodes can act independently and partial solutions are integrated by communication between nodes, often asynchronously. By virtue of their scale, DAI systems are robust and elastic, and by necessity, loosely coupled. Furthermore, DAI systems are built to be adaptive to changes in the problem definition or underlying data sets due to the scale and difficulty in redeployment. DAI systems do not require all the relevant data to be aggregated in a single location, in contrast to monolithic or centralized Artificial Intelligence systems which have tightly coupled and geographically close processing nodes. Therefore, DAI systems often operate on sub-samples or hashed impressions of very large datasets. In addition, the source dataset may change or be updated during the course of the execution of a DAI system.

Goals

The objectives of Distributed Artificial Intelligence are to solve the

reasoning Reason is the capacity of consciously applying logic by drawing conclusions from new or existing information, with the aim of seeking the truth. It is closely associated with such characteristically human activities as philosophy, science, lang ...

, planning, learning and perception problems of

, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires: * A distributed system with robust and elastic computation on unreliable and failing resources that are loosely coupled * Coordination of the actions and communication of the nodes * Subsamples of large data sets and online machine learning There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. Mainstream problems in DAI research include the following: * Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that

multiprocessor Multiprocessing is the use of two or more central processing units (CPUs) within a single computer system. The term also refers to the ability of a system to support more than one processor or the ability to allocate tasks between them. There ar ...

systems and clusters of computers can be used to speed up calculation. * Distributed problem solving (DPS): the concept of

agent Agent may refer to: Espionage, investigation, and law *, spies or intelligence officers * Law of agency, laws involving a person authorized to act on behalf of another ** Agent of record, a person with a contractual agreement with an insuran ...

, autonomous entities that can communicate with each other, was developed to serve as an

abstraction Abstraction in its main sense is a conceptual process wherein general rules and concepts are derived from the usage and classification of specific examples, literal ("real" or " concrete") signifiers, first principles, or other methods. "An a ...

for developing DPS systems. See below for further details. * Multi-Agent Based Simulation (MABS): a branch of DAI that builds the foundation for simulations that need to analyze not only phenomena at

macro Macro (or MACRO) may refer to: Science and technology * Macroscopic, subjects visible to the eye * Macro photography, a type of close-up photography * Image macro, a picture with text superimposed * Monopole, Astrophysics and Cosmic Ray Observat ...

level but also at micro level, as it is in many

social simulation Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in computational law, psychology, organizational behavior, sociology, political science, e ...

scenarios.

History

In 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interactions of intelligent agents Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperation, by coexistence or by competition. DAI is categorized into Multi-agent systems and distributed problem solving In

Multi-agent system A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents.Hu, J.; Bhowmick, P.; Jang, I.; Arvin, F.; Lanzon, A.,A Decentralized Cluster Formation Containment Framework fo ...

s the main focus is how agents coordinate their knowledge and activities. For distributed problem solving the major focus is how the problem is decomposed and the solutions are synthesized.

Examples

Multi-agent systems and distributed problem solving are the two main DAI approaches. There are numerous applications and tools.

Approaches

Two types of DAI has emerged: * In

s agents coordinate their knowledge and activities and reason about the processes of coordination. Agents are physical or virtual entities that can act, perceive its environment and communicate with other agents. The agent is autonomous and has skills to achieve goals. The agents change the state of their environment by their actions. There are a number of different coordination techniques * In distributed problem solving the work is divided among nodes and the knowledge is shared. The main concerns are task decomposition and synthesis of the knowledge and solutions. DAI can apply a bottom-up approach to AI, similar to the

subsumption architecture Subsumption architecture is a reactive robotic architecture heavily associated with behavior-based robotics which was very popular in the 1980s and 90s. The term was introduced by Rodney Brooks and colleagues in 1986.Brooks, R. A., "A Robust Pro ...

as well as the traditional top-down approach of AI. In addition, DAI can also be a vehicle for

emergence In philosophy, systems theory, science, and art, emergence occurs when an entity is observed to have properties its parts do not have on their own, properties or behaviors that emerge only when the parts interact in a wider whole. Emergen ...

Applications

Areas where DAI have been applied are: * Electronic commerce, e.g. for trading strategies the DAI system learns financial trading rules from subsamples of very large samples of financial data * Networks, e.g. in

telecommunication Telecommunication is the transmission of information by various types of technologies over wire, radio, optical, or other electromagnetic systems. It has its origin in the desire of humans for communication over a distance greater than tha ...

s the DAI system controls the cooperative resources in a WLAN network http://dair.uncc.edu/projects/past-projects/wlan-resource *

Routing Routing is the process of selecting a path for traffic in a network or between or across multiple networks. Broadly, routing is performed in many types of networks, including circuit-switched networks, such as the public switched telephone netw ...

, e.g. model vehicle flow in transport networks *

Scheduling A schedule or a timetable, as a basic time-management tool, consists of a list of times at which possible tasks, events, or actions are intended to take place, or of a sequence of events in the chronological order in which such things are i ...

, e.g. flow shop scheduling where the resource management entity ensures local optimization and cooperation for global and local consistency * Multi-Agent systems, e.g.

artificial life Artificial life (often abbreviated ALife or A-Life) is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemist ...

, the study of simulated life * Electric power systems, e.g. COndition Monitoring Multi-Agent System (COMMAS) applied to transformer condition monitoring, and IntelliTEAM II Automatic Restoration System

Tools

ECStar
a distributed rule-based learning system

Agents and Multi-agent systems

Notion of Agents: Agents can be described as distinct entities with standard boundaries and interfaces designed for problem solving. Notion of Multi-Agents:Multi-Agent system is defined as a network of agents which are loosely coupled working as a single entity like society for problem solving that an individual agent cannot solve.

Software agents

The key concept used in DPS and MABS is the abstraction called software agents. An agent is a virtual (or physical)

autonomous In developmental psychology and moral, political, and bioethical philosophy, autonomy, from , ''autonomos'', from αὐτο- ''auto-'' "self" and νόμος ''nomos'', "law", hence when combined understood to mean "one who gives oneself one's ...

entity that has an understanding of its environment and acts upon it. An agent is usually able to communicate with other agents in the same system to achieve a common goal, that one agent alone could not achieve. This communication system uses an agent communication language. A first classification that is useful is to divide agents into: * reactive agent – A reactive agent is not much more than an automaton that receives input, processes it and produces an output. * deliberative agent – A deliberative agent in contrast should have an internal view of its environment and is able to follow its own plans. * hybrid agent – A hybrid agent is a mixture of reactive and deliberative, that follows its own plans, but also sometimes directly reacts to external events without deliberation. Well-recognized agent architectures that describe how an agent is internally structured are: * ASMO (emergence of distributed modules) * BDI (Believe Desire Intention, a general architecture that describes how plans are made) * InterRAP (A three-layer architecture, with a reactive, a deliberative and a social layer) * PECS (Physics, Emotion, Cognition, Social, describes how those four parts influences the agents behavior). * Soar (a rule-based approach)

Challenges

The challenges in Distributed AI are: 1.How to carry out communication and interaction of agents and which communication language or protocols should be used. 2.How to ensure the coherency of agents. 3.How to synthesise the results among 'intelligent agents' group by formulation, description, decomposition and allocation.

References

* A. Bond and L. Gasser. Readings in Distributed Artificial Intelligence. Morgan Kaufmann, San Mateo, CA, 1988. * Brahim Chaib-Draa, Bernard Moulin, René Mandiau, and P Millot. Trends in distributed artificial intelligence. Artificial Intelligence Review, 6(1):35-66, 1992. * Nick R Jennings. Coordination techniques for distributed artificial intelligence. Foundations of distributed artificial intelligence, pages 187-210, 1996. * Damien Trentesaux, Philippe Pesin, and Christian Tahon. Distributed artificial intelligence for fms scheduling, control and design support. Journal of Intelligent Manufacturing, 11(6):573-589, 2000. * Catterson, V. M., Davidson, E. M., & McArthur, S. D. J. Practical applications of multi-agent systems in electric power systems. ''European Transactions on Electrical Power'', ''22''(2), 235–252. 2012