Computational Intelligence - November 2013 - 28

standable by operators of agents. In the deliberation process a balance is created between
the amount of real-time planning and use of a
priori plans that are available. This enables
seamless blending of fast reactive behavior and
slow "contemplative" evaluation of long term
consequences of agent actions and environmental events.

Deliberation of an agent is its decision making to
chose which of its plans and skills to execute at any
moment of time to achieve its goals.
progress in terms of new features or strengthening existing
features. Our architecture enables a programmer to equip the
agent with the intelligence features classified in [40] as cognitive intelligence, social intelligence, behavioral intelligence,
ambient intelligence, collective intelligence and genetic intelligence. We also emphasize the existence of shared concepts and
understanding between humans and the agents and the agent's
natural ability to explain why it has chosen a given action.
Another important consideration is simplicity and sharability
of agent code during the development and easy maintenance
during its use.
architectural approach
A model-based approach is taken for handling data with structural templates provided by an ontology of the agent. Class
names and their attributes are precise professional terms under-

Abstractor of
Higher Level States
and Reasoning by
Logic Inference
r

Sensor/Perception
Stream

E
Abst. r1

Context }1

Plan 1

Abst. r2

Context }2

Plan 2

Abst. r3

Context }3

Plan 3

Abst. r4

Context }4

Plan 4

Abst. rN

Context }5

Plan N

programming paradigms and languages
Both declarative and procedural programming is used in three
layers: natural language program (NLP) compiles into embedded MATLAB code that compiles into standard Java or C++.
At the top level abstractions expressed in natural language programming (sEnglish) form layers of abstractions to define
operations that lend themselves to human interpretation. In
principle a similar route can be used to compile natural language into declarative rational agent code which then runs on
a Java-based interpreter. In this paper we focus on declarative
agent code in the Gwendolen language which must be created
directly by the programmer, however we have also investigated
the use of the Jason agent language and the programming of
Jason agents using the same natural language interface as that
which links to MATLAB.
Deployment architecture
Distributed homogeneous and also TCP/IP connected heterogeneous sets of processors can be used where Java and C++/
ROS can run. The primary approach is soft-real time but hard
real time implementation is also feasible.
Development Environment
The current development environment is a mixture of Eclipse
(sEnglish/Java/Gwendolen), MATLAB/Simulink and the
ROS/C++ development systems.
Documentation
The high level code for agent capabilities is written using natural language programming. The BDI agent code is in a declarative language such as Gwendolin or Jason, also presentable
using sEnglish. Low level code is in MATLAB/C++/Java and
documented in standard ways. The natural language descriptions of capabilities facilitate communication within the development team, reduce the effort for users and enable the creation of information repository to capture the development
process for future use and maintenance.
III. Programming Paradigm Descriptions

Figure 1 Operational schematic of a deliberative architecture: yellow blocks represent executable plans that modify the environment
E where an agent operates. A perception stream from the environment is abstracted and evaluated against contexts and abstractions.
A plan selector function, a, results in the chosen executable plan P.

IEEE ComputatIonal IntEllIgEnCE magazInE | novEmbEr 2013

Our research has focused on a novel agent architecture for
autonomous systems. The broad operation of the architecture
considered here is illustrated in Figure 1, where an agent may
invoke the execution of an action encapsulated within the set
of available agent plans P, based upon the set of raw data available from the agent's sensors, W E, and the set of discrete statements that represent abstractions of the raw data in W E, L, is
formally defined here as:

Table of Contents for the Digital Edition of Computational Intelligence - November 2013