Computational Intelligence - November 2013 - 27

Within the PRS framework, a knowledge
An agent is a software that enables a robot to make
database detailing how to achieve particular
goals or react to certain situations is interfaced
its own decisions to achieve some goals, plan ahead
by an interpreter. In its concept this is similar
and keep itself to behavior rules.
to a horizontally layered Touring machine [10]
with parallel layers for modelling, planning
tem (Livingstone). In keeping with the ethos of agent systems,
and reactive behavior, though notions of intention are conrather than being commanded to execute a sequence of comtained and consequently place PRS as a deliberative BDI
mands, the system design of Remote Agent was such that the
model. InteRRap is a hybrid BDI architecture with three sepaattainment of a list of goals was sought [38].
rate layers incorporating behavior, local planning and cooperative planning. These interface the world state, model and social
knowledge bases, respectively. These knowledge bases are in
E. Hybrid Automata Versus Deliberative Agents
turn fed through perception and communication processes.
The potential state explosion resulting from replicated plan
Ultimately it is the lowest level behavioral layer that is responsirepresentations within discrete states of the hybrid system is
ble for environmental interaction, though this is augmented by
touched upon in [20] and discussed in more detail in [39].
higher levels that involve deliberation processes to enable the
Moreover, in constructing a deliberative agent from the perattainment of high level goals [16].
spective of hybrid automata involves a loss of expressivity; the
representation as hybrid automata often loses the explicit differentiation between what the agent can do and what it will actuD. Hierarchical Planner and Executor Systems
ally choose to do. Abstraction systems in hybrid automata are
Two closely related frameworks, MDS (Mission Data System)
implicit. This makes system operations more difficult to underand CLARAty (Coupled Layer Architecture for Robotic
stand, especially when things go wrong.
Autonomy) [17], provide frameworks for the development of
goal-based systems intended for, though not limited to,
robotic space systems. MDS is an architectural framework
F. Planner-Executive Systems Versus Deliberative Agents
encapsulating a set of methodologies and technologies for
Deliberative agents have a large number of preprogrammed
the design and development of complex, software intensive,
plans and focus on selecting the appropriate plan for execution
control systems on a variety of platforms that map high level
given the current situation. Some deliberative agent approaches
intentions (goals) through to actions. The system architecture
such as Jason [13] and Jade [31] can also generate their own
focuses on expressing the physical states that a control system
plans, or access sub-systems dedicated to planning based on the
needs to manage and the interactions between these states
underlying capabilities of the systems. Other systems, such as
[36]. CLARAty is a two-layer object oriented software infraCLARAty [17], implement continuous planning and execution
structure for the development and integration of robotic
based approaches that consequently makes planning the centre
algorithms. Its prime purpose is to provide a common reuspiece of an agent's existence.
able interface for heterogeneous robotics platforms by using
Deliberative agents use a "reasoning cycle" which first
established object oriented design principles. In a CLARAty
assesses the current situation using reasoning by logic and
architecture two layers separate a mainly declarative programthen selects and/or executes a plan. This assessment is based
ming within a "Decision Layer" and a mainly procedural proupon anthropomorphic concepts such as beliefs, goals and
gramming within a "Functional Layer". It is the functional
intentions. Planning procedures and temporal-logic-based
layer that encapsulates low and mid-level autonomous capahypothetical inference (about consequences of future actions)
bilities, whereas the decision layer encapsulates global reasoncan be accommodated within these deliberative agent frameing about system resources and mission constraints. Both
works. Two-layer planner/executor-functional agents, on the
MDS and CLARAty seek the application of a generic frameother hand, focus all their activity on efficient real-time
work for the development of goal based systems on heteroreplanning. Instead of complete replanning, deliberative
geneous platforms. More specifically, MDS seeks to link sysagents can have access to plan-libraries defined for them as
tem abstractions between systems engineers and software
their knowledge and skill base. In many ways planner-execuengineers through model based design; CLARAty seeks to
tive systems and deliberative agents do the same job in differpromote reusable code to enable advancement of robotic
ent ways but the most striking difference is in terms of
control algorithms.
human like reasoning in deliberative agents that tends to
An interesting "middle ground" between BDI systems and
make them more able to collaborate with humans operators.
complex decision trees is the Remote Agent technology
deployed on the DeepSpace1 technology proving mission [37].
G. Features of Our Approach
Remote Agent, an agent system based upon model-based diagCreating a new software architecture with significant benefits
nosis (MBD), integrated three separate technologies: an onfor autonomous robot control is a difficult problem when
board planner-scheduler (EUROPA), a robust multi-threaded
there are excellent software packages around [11, 13, 31, 14,
executive, and a model-based fault diagnosis and recovery sys17]. In this section we identify the possible aspects of further

November 2013 | Ieee ComputatIoNal INtellIgeNCe magazINe

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