Intelligent Agent part 2: Architecture

Here the second part of my research study on software intelligent agent.

Intelligent Agent Architecture

Current agent architecture can be classified as either 1) logical based agent, 2) reactive agent, 3) BDI agent (belief-desire-intention) and 4) layered architectures. The following summarizes very briefly these approaches and provides some insight in relation to their pros/cons.

The logic based approach is when the decision making strategy reduces to a problem of proof (predicate logic), i.e. the agent uses its symbolic representation of the world (usually through logical formulae) to accomplish logical deduction in order to select an appropriate action. In this scheme, the deduction rules that determine the agent behaviour are the “program” (execution), whereas the symbolic information/ representation of the environment is the current database (data).

This traditional approach originating from early AI work, suffers from disadvantages such as the following:

1. Because of Calculative rationality (the optimal action is deduced at time j based on the environment condition as seen at time i, where j>i), we must assume that the environment did not change during the time agent chooses its action, reducing the practicality of such agents. Although alternative compilation of the logical specification into a more amenable form can provide better efficiency, this issue still persist in time-constraint and highly dynamic environment.

2. Perception of the agent is realised through a non-obvious mapping from real environment condition to a set of symbolic formulae. It has been acknowledged that as seductive and useful logic is to conceptualise and specify agent, it is quite difficult when one tries to represent and reason about complex, dynamic of physical or non-physical environment using a set of explicit symbolic reasoning.

The reactive agent architecture (alternatively referred to as behavioural or situated) proposed by R. Brooks intends to simplify and avoid the difficulty of symbolic representation by providing a more economical, elegant and robust agent whose behaviour is simply a function of its local environment. One of the most famous technique called Subsumption architecture is characterized by an action-perceptual input mapping (situation  action) and a layer controlling hierarchy in charge of resolving possible simultaneous action conflicts (lower layer action takes precedence over higher or more abstract layer). Agent performance and complexity which depends on the number of behaviour and number of percept (raw sensor input is used as opposed to complex symbolic representation) can result in a constant decision time when hard-wiring decision making process, making such agent computational simple and attractive. However, this simplicity leads to some disadvantages:

1. Only a local environment information is gathered making non-local information not possibly taken into account

2. How could purely reactive paradigm lead to some form of experience learning and performance improvement over time?

3. Implementation engineering relying heavily on experimentation, trail and error methodology

4. The hierarchical form of action control can lead to confusion when a large number of different behaviours are modelled.

The BDI or Belief-Desire-Intention architecture is influenced by the study of practical reasoning (human reasoning taking place during continuous decision-action process). This philosophical concept explains any action taken by a combination of intention (what goals are to be achieved) and a chosen option (how to proceed for achieving these goals). It is assumed in BDI system that course of action taken to satisfy goals should not stop simply because of failure but in the mean time it should not persist without any reasonable chance of success (or without the original intention remaining relevant). This represents a dilemma between the right balance of reactiveness and proactiveness behaviour contained in a BDI agent. Pure goal directed agents will never stop to reconsider their intention even after important environment changes that could invalidate the original goal. Meanwhile pure event driven agents never spend enough time trying to achieve their goals and waste all computational time in reacting to their environment. In general the more static an environment is the more pro-active agents should be, whereas in more dynamic environment agents need to have the ability to react to changes.

The layered architecture aims to accomplish a better balance between agent pro-activeness and re-activeness by introducing the concept of specialized dedicated layer. Layering architecture into subsystems allows one to design specialized subsystems adapted for specific task, for example one subsystem would deal with reactive behaviour while another one with pro-active behaviour. Most of these architectures use either horizontal layering or vertical layering for their information and control flow mechanisms. Horizontal layering requires some form of mediation between all layers since they all have access to input and can all produce some action output. Vertical layering involves a flow sequence between all layers, with lowest level layer receiving the input and forwarding it to highest or more abstract layer if necessary (i.e. when current layer expertise and competence cannot process or deal with input). Input information flowing from lowest to highest is referred to by bottom-up activation while command flowing from highest to lowest is referred to by top-down execution. This architecture is very popular due to its pragmatism and intuitive mapping from pro-active, reactive, social behaviours into specific and dedicated subsystems.

Taken from

1. “Multiagent Systems, a Modern Approach to Distributed Modern Approach to Artifical Intelligence” Chapter 2 Mutliagents Systems and Societies of Agents, Huhns and Stephens (1999)