Artificial Intelligence

Artificial intelligence is an advanced technique used to solve problems that are difficult, or simply impractical, for a monolithic model to solve (Santa-Eulalia, 2009). It refers to the branch of computer science concerned with making computers behave like humans. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology (Webopedia). Artificial intelligence includes, among other things, robotics, neural networks and agent-based systems. With these advanced approaches, it is possible to solve problems at an expert level by utilizing the knowledge and problem solving logic of human experts.

From artificial intelligence, multi-agent-based systems use learning aspects, social capabilities and others. Normally, agents encapsulate one or more techniques from supply chain optimization and supply chain simulation to perform their activities, but they usually go beyond by including additional issues, such as negotiation protocols and learning algorithms.

Agent and multi-agent systems

An agent is an abstraction to represent complex entities and it is the main building block to form a multi-agent-based system. The concept may be very popular today, but defining it is not simple since it encompasses concepts from many domains, such as distributed artificial intelligence, distributed computing, social network theory, cognitive science, operational research, etc. One of the most broadly accepted definitions found in the literature may be that offered by Jennings and Wooldridge (1998): “an agent is a computer system situated in some environment, and that is capable of autonomous action in this environment in order to meet its design

objectives”. Wooldridge (1998) complements this definition by detailing some properties of agents:

 Autonomy: autonomy refers to the capacity to act without the intervention of humans or other systems: they have control over both their own internal state and their behaviour;

 Reactivity: agents are situated in an environment. An environment can be the physical world, a user (via a graphical user interface), a collection of other agents, the Internet and, sometimes, many of these elements combined. In addition, agents are able to perceive this environment in order to respond in a timely fashion to changes occurring in the environment;

 Pro-activeness: agents do not just act in reaction to their environment; they are able to show goal-directed behaviour in which they can take initiatives;

 Social ability: agents interact with other agents, and normally have the ability to engage in social activities (e.g., cooperative problem solving or negotiation) in order to achieve their goals.

The environment of an agent is normally composed of other agents with whom it interacts. These agents together form an agent society or a multi-agent system. This set of agents works together in order to accomplish certain tasks. All of them use their competences and knowledge to strengthen the capacity of solving problems.

Agents use sophisticated interaction schemas that include cooperation capability, coordination capability and negotiation capability. In order to perform their interactions, they have to communicate. Communication makes use of certain mechanisms, mainly:

 Communication model: the way in which agents can communicate with each other, identifying information exchange synchronism and the interrelation scheme among agents.

 Message communication language: for the definition of a set of performatives and their meaning to support the communication process.

 Knowledge representation language: for message content. This type of language establishes conditions to the creation of knowledge databases and allows knowledge manipulation by inference machines. Diverse knowledge has to be represented, such as domain knowledge, knowledge about the environment, self-knowledge, etc.

 Ontology: gives semantics to a conversation, i.e., a formal explicit specification of a shared conceptualization.

In the context of supply chains, Operations Research (OR) and agent technology can be combined to capture the complexity of supply chain modelling. OR proposes a

between decision variables. In other words, OR recognizes the input/output relationships of a supply chain system, linking these relationships with performance metrics and decision options.

Agents can also be used to model the system’s input/output behaviour. When the system becomes too complex, various reactive agents can be used to model several of its parts; together they model the behaviour of the entire system. They can, for instance, model real life entities, including human decision-makers and artificial entities, such as organizational units or functional software modules, in order to capture more complex supply chain behaviours.

As OR proposes a large body of literature to model various supply chain problems, both approaches (agents and OR) can be used together to propose accurate and detailed models of supply chains. The intuitive way to do so is to encapsulate OR models and tools into software agents that represent either planning functions or organizational units. These models and tools are then used to model part of the decision-making ability of agents. However, the functional ability to use these tools, according to the situation, still needs to be modelled in the agent (Forget et al., 2008).

Over the last few years, FORAC has developed such an agent-based planning and simulation environment. This platform aims to address the ability to plan and coordinate operations throughout the supply chain, and the ability to analyze the dynamics and simulate different supply chain scenarios through simulation. It allows the user to evaluate and compare different planning models, coordination mechanisms or supply chain configurations, according to user specified performance measures (Frayret et al., 2007).