Computational Intelligence - November 2013 - 26

missions [4, 5]. The former, Hayabusa, was a sample return mission that targeted the Itokawa Near Earth Object and successfully
returned a soil sample to Earth in 2010.The latter mission, Dawn,
is a current mission targeted at the proto-planets Vesta and Ceres
that reside in the Asteroid belt between Mars and Jupiter.
Although successful, the Hayabusa mission ran into problems that
threatened its completion: in 2003 a solar flare damaged the solar
panels onboard the spacecraft reducing the efficiency of the propulsion system; in 2005 two reaction wheels failed, compromising
the attitude controllability for the remainder of the mission; the
release of the "Minerva" probing robot failed; a communication
blackout of nearly two months was caused by a fuel leakage; and,
in 2009 during the return cruise, an ion engine anomaly was
detected, which took 15 days to circumvent. The mission phases
and operations of Hayabusa were controlled from Earth and this
was a contributing factor to the failed "Minerva" probe release:
because of the communication time lag of 32 minutes between
the ground station and the spacecraft, the command to release the
probe was received during an automatically triggered ascent
phase from the asteroid and the probe was consequently lost
in space.
These events, encountered during the Hayabusa mission,
highlight the need for robotic platforms to be capable of reliably performing localized decision making to enable completion of their tasks while immersed in a dynamic, unknown, and
possibly hostile environment.
The agent programming system to be presented here develops the anthropomorphic belief-desire-intention agent programming approach further by enabling efficient hierarchical
planning and execution capabilities. There are five important
benefits: (1) the method we propose simplifies agent operations
relative to multi-layer agents by blending reactive and foresight
based behaviors through logic based reasoning. (2) Agent operations such as sensing, abstractions, task executions, behavior rules
and reasoning become transparent for a team of programmers
through the use of natural language programming. (3) The ability to use English language descriptions to define agent reasoning also means that operators and agents can have a shared
knowledge of meanings and procedures. (4) In our system it is
also straightforward to program an agent to make it able to
explain its selected actions or its problems to its operators. (5)
Despite its user friendliness, our system is formally verifiable by
model checking methods to ensure our agents always try to do
their best.
The improvements proposed here signify important practical benefits for engineers designing and operating such autonomous systems.
II. Programming Approaches to Autonomous Systems

Current approaches to programming autonomous robot operations fall under the closely related domains of:
1) programming hybrid automata [6, 7, 8, 9]
2) agent oriented programming [10, 11, 12, 8, 13, 14]
3) programming vertical-horizontal multi-layered systems
[15, 16]

IEEE ComputatIonal IntEllIgEnCE magazInE | novEmbEr 2013

4) programming hierarchical planners and executors [17, 18].
In this section a brief review is given that is followed by a
list of the features of our programming system.
A. Hybrid-Automata-Based Autonomy

Hybrid systems model computer controlled engineering systems that interact with continuous physical processes in their
environment [19, 7]. Efforts to model agent systems as a series
of interconnected hybrid automata have been made [20, 21, 22]
and the commercial software, State FlowTM [23], is widely used
by industry. This motivated agent development using hybridautomaton based models [24].
To implement a deliberative agent system through a set of
hybrid automata would entail the representation of each of the
available plans of the agent as a hybrid automaton. The resulting
system would become very complex and a multi-agent system
would be expressed as a large parallel set of concurrent automata.
B. Multi-Layered Agents

Multi-layered agent systems aim to combine the timely nature
of reactive architectures (hybrid automata) with the analytic
approach to the environment that takes more time [25, 15].
Consequently, as their name would suggest, these systems
involve a horizontal or vertical hierarchy of interacting subsystem layers. The complexity of potential information bottlenecks, and the need for internal mediation within purely horizontal architectures, are partially alleviated by adding vertical
architectures, though these structures do not easily provide for
fault tolerance [26, 16]. Knowledge based deliberation within
agent systems is performed with logical reasoning over symbolic definitions that explicitly represent a model of the world
in which the agent resides and is encapsulated by the "intentional" stance, wherein computational (agent) reasoning is subject to anthropomorphism. This is a promising approach for a
system to cope with the computational complexities associated
at high levels of automated decision making [27]. Although
there is no all-encompassing agent theory for multi-layered
agents, significant contributions have been made concerning
the properties an agent should have and how they should be
formally represented and reasoned [28, 29].
C. Agent Oriented Programming

Logical frameworks in which beliefs, desires and intentions
(BDI) are primitive attitudes, as developed by Bratman and following the philosophy of Dennet [30, 28], are a popular format
for deliberative systems. Deliberative architectures, and their
logical foundations, have been thoroughly investigated and used
within numerous agent programming languages, including
AgentSpeak, 3APL, GOAL and CogniTAO as well as the Java
based frameworks of JADE, Jadex and mJACK [11, 31, 32, 33,
13]. Possibly the most widely known BDI implementations are
the procedural reasoning system (PRS) and InteRRaP [14].
PRS is a situated real-time reasoning system that has been
applied to the handling of space shuttle malfunctions, threat
assessment and the control of autonomous robots [34, 35].

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