=Paper=
{{Paper
|id=Vol-1788/STIDS2016_T07
|storemode=property
|title=Effects-Based Air Operations Planning Framework: A Knowledge-Based Simulation Approach
|pdfUrl=https://ceur-ws.org/Vol-1788/STIDS_2016_T07_Costa_Costa.pdf
|volume=Vol-1788
|authors=André N. Costa,Paulo C. G. Costa
|dblpUrl=https://dblp.org/rec/conf/stids/CostaC16
}}
==Effects-Based Air Operations Planning Framework: A Knowledge-Based Simulation Approach==
https://ceur-ws.org/Vol-1788/STIDS_2016_T07_Costa_Costa.pdf
Effects-Based Air Operations Planning Framework:
A Knowledge-Based Simulation Approach
André N. Costa1,2 Paulo C. G. Costa2
1
Institute for Advanced Studies 2
C4I & Cyber Center
Brazilian Air Force George Mason University
São José dos Campos, SP, Brazil Fairfax, VA, USA
anegraoc@c4i.gmu.edu pcosta@gmu.edu
Abstract— Planning air warfare operations has always been a operators become scarce resources that usually are centralized
complex endeavor. However, as technology evolves at an and not easily accessible by those who conduct the operations.
increasingly fast pace, so does the complexity of managing its
resources. In modern air operations, planners have to deal with a To put from a different perspective, this centralization of the
highly changing environment influenced by enemy air defenses, planning resources results in distancing the plan development
weather forecasts, among many other factors, demanding much from those who will execute it, since the required planning
effort to handle the great number of constraints and uncertainties resources are hardly available on the operations commands. It
presented by them. As a result, a number of decision-support also impacts the agility of the process, especially when
systems have emerged attempting to facilitate the planning of air considering highly dynamic mission planning contexts that
warfare operations. These systems usually rely on a wide variety usually require re-planning to address emerging situations.
of methodologies, which sometimes present a challenge in
themselves when it comes to assessing the feasibility and The framework presented in this paper leverages semantic
effectiveness of the produced plans. Computer simulations are a technologies to formalize the knowledge required for planning
practical way of providing this assessment, usually by running the air warfare operations. The reasoning behind this approach is
resulting plans multiple times and checking the results against key two-fold, (1) to avoid the need for highly trained system
criteria. Yet, establishing the right criteria, properly accounting planners, and (2) to decentralize the plan building and evaluation
for the “fog of war,” and avoiding impractical simulation run process, thus reducing the dependence on heavy computing
times and costs are still major challenges. This paper addresses power.
such challenges by proposing the development of a decision-
support framework that combines ontology-based agile knowledge The planning knowledge to be captured is based on the
and a simulation-based mission planning methodology that Effects-Based Approach to Operations (EBAO). This paradigm
accounts for the inherent uncertainties that air operations face. has been extensively sought by several research and
We avoid costly computation times required by simulation- development efforts in the last decades, paving the way to its
intensive course-of-action analyzers by initially pruning the fruition and further application on the field [1]. According to [2],
solution space through ontological reasoning. Moreover, the “EBAO informs every aspect of how the Air Force designs,
approach complies with the Effects-Based Approach to plans, executes, assesses, and adapts operations.” Therefore, it
Operations, having a clear correspondence of processes with it. should guide any framework that proposes to aid the planning
The explanations are focused on a specific scenario concerning process of air operations. Following this premise, the backbone
intelligence, surveillance, and reconnaissance operations. of the matchmaking process within this work is the Effects-
Based Approach to Planning (EBP), which ultimately defines
Keywords—ontologies; effects-based planning; modeling and the semantic description of the domain and how it relates to the
simulation; semantic matchmaking planning procedure.
I. INTRODUCTION Once the initial states of the EBAO knowledge is made
The fast pace by which complexity of current military explicit through ontology engineering, the focus of our
operations is increasing has become a major challenge for development becomes to provide a solution that does not require
mission planners, requiring a much more meticulous planning large amounts of computing power and time. Rather, it may be
process to handle all the factors that might influence the done using portable computers by the operations planners. We
outcomes of an operation. Several planning methodologies have achieve that by leveraging the EBAO mission concepts via a
been observed in the last years, yielding a number of systems to logical engine that pre-selects the possibilities given the
support air operations planning. To deal with complexity, these planning data provided, greatly limiting the solution search
systems usually rely on heavy computing power, as well as on space. This way, optimization methods can be used in a much
specialized operators that must be highly trained in the more effective way, applied to scenarios that are simulated in a
methodology associated with the system. Such requirements simplified fashion, and allowing for a quick means of assessing
make the planning process brittle, as both the hardware and the the optimization parameters. In a second step, these generated
low-resolution solutions are evaluated based on criteria derived
STIDS 2016 Proceedings Page 55
from the EBAO ontology. The most promising ones then go Rosenberg et al. [4] suggest a collection of decision-support
through a more complex simulation environment that, through tools for planning generation that consists of “a method to
entity-level simulation, would provide a much more detailed define an operational scenario, an optimization engine to
outcome that includes mission-specific prognostics. generate a diverse set of solutions, and a suite of visualization
and analysis tools to review, analyze, and visualize generated
For providing a clearer view of how this framework can be
plans.” To provide a solution in a timely manner, the authors
operationalized, an ISR (intelligence, surveillance, and propose a rapid evaluation of candidate solutions through agent-
reconnaissance) scenario is built with unclassified data from the
based modeling and simulation (ABMS). They leveraged the
Brazilian Air Force. The database includes a number of same software used in our proposed framework.
platforms and sensors that have to be assigned to tasks, leading
to actions that generate the desired operational effects. Since not Similarly, [5] – also using the same software application –
all sensing sources can provide the needed information to task focuses on a Joint Suppression of Enemy Air Defenses (JSEAD)
requirements, because the sources are context sensitive [3], this scenario in which plans are generated, optimized, and simulated.
assignment can be very challenging to the planning staff. The authors also rely on an ABMS of two sides containing
different types of entities, usually targets and air defenses for the
ISR operations proved to be a good choice for this initial opposing side and strikers and JSEAD units for the friendly side.
scenario, since they contain multiple factors that directly affect
Their results illustrated the potential of low-resolution
the planning process, and also make its optimization very simulation as a rapid evaluation tool of generated plans, which
important. Also, since ISR assets are oftentimes highly complex
will be in time described within our own approach.
and valuable, a less than adequate planning will lead to the loss
of costly flight hours and very specialized crews work. Unlike in our approach, these research efforts do not apply
Nevertheless, the application on the planning process of other semantic methods as a form of structuring the modeling and
air operations can be made based on the same framework simulation process (e.g. [6]), or as a conceptual basis for the
structure, converting the sensor matchmaking phase to a weapon framework as those in the subsection below.
to target matching in the case of airstrike or managing electronic
warfare (EW) measures on a suppression of enemy air defense B. Semantic Matchmaking Framework
(SEAD) mission, both sharing many complexities with ISR The literature on assigning sensors to missions or tasks is
operations. vast, but the use of semantic techniques for this purpose is rather
limited. Therefore, it is worth pointing out [7], which advocates
At this stage of our development, we did not yet reach full for an ontological problem-solving architecture to facilitate
circle or obtained conclusive results of a detailed simulation. automated inference of assigning sensors to missions. This work
Thus, our focus on this paper is to provide the long-range vision limits the solution domain as a means of including a
of the framework, its goals, and an overview of the technical coordination system to emulate the assets and complete bears
approaches determined by our preliminary research efforts to similarities with the aforementioned planning simulation
solve the challenges encountered so far. frameworks and the one we propose in this paper.
The paper is organized as follows. Section II gives a brief One of the most productive solutions is sponsored by the
overview of previous research on operation planning U.S. Army Research Laboratory and the United Kingdom
frameworks like the one proposed, as well as on semantic Ministry of Defence ([8], [9], [10], [11], [12], [13]). Its authors
matchmaking efforts. Section III provides the main concepts conceive a system that relies on a series of ontologies for
involved on EBAO, emphasizing the EBP process. Section IV assigning sensors to missions. The backbone of this process is
presents the framework, including the description of the the “Mission and Means Framework” that is claimed to “provide
software applications to be used on its implementation. Section a model for explicitly specifying a military mission and
V focuses explicitly on the semantic part of the framework. quantitatively evaluating the mission utility of alternative
Section VI displays the considered scenario, describing warfighting solutions” [12]. The three basic elements of their
resources and critical conditions that may influence how the methodology are [14]:
image requirements can be met. Finally, Section VII
summarizes the paper, pointing to the next steps to be taken. • Top-to-bottom solution to the problem of deploying
sensors to meet the information needs of tasks in a
II. BACKGROUND mission context;
A. Planning Simulation Framework • Combination of reasoning at mission-planning time, and
Several planning frameworks are available within the optimization algorithms at mission execution-time; and
military research context. However, much of the work available
• Dynamic deployment configuration of selected sensor
is either too complex or remains inaccessible (e.g., classified).
instances by means of a sensor infrastructure.
The complexity is often directly related to the high resolution
required to generate reliable results. The work includes modular ontologies that cover task
requirements, sensor capabilities, and a structured framework to
While trying to present an alternative to this complexity
associate tasks with sensors. The ontologies specify the
problem, some authors have proposed the use of lower
requirements of the missions and the capabilities of the sensors
resolution simulation and optimization methodologies, which
so that the framework is able to decide between combinations of
deal with less factors at a time.
sensors to satisfy the requirements of a given mission [12].
STIDS 2016 Proceedings Page 56
Even though providing a proven assignment system [3], with uncertainties contained in the air operations. EBP has to fully
very well-structured ontologies, this work does not focus on confront the scope and magnitude of these uncertainties,
dealing with the uncertainties that a planning scenario presents. especially when dealing with outcome predictions.
This is due to the use of logical reasoners and mostly
deterministic functions. Stochasticity is not considered, just B. Qualitative modeling
comparisons between deterministic possibilities. In addition, the Secondly, in this uncertainty-sensitive framework it is
Mission and Means Framework ontology focuses on tasks imperative to possess a trustworthy qualitative modeling,
instead of effects. Thus, although providing a direct and clear including frictional, credibility and cognitive factors that are
way of breaking down missions [9], the approach does not oftentimes closely related to indirect effects. This is highly
emphasize the holistic view advocated in our work. Finally, [15] dependable on the availability of subject matter experts (SME)
also provides more details on how this framework may be to provide information about systems and operations.
structured as an ontology.
C. Agent-based modeling
III. EFFECTS-BASED PLANNING
The qualitative modeling also requires a focus on decision-
Even though utilizing the Mission and Means Framework, making, which can be addressed by agent-based modeling
[9] states that alternative mission planning approaches, such as approaches, accurately depicting the C4ISR aspects of the
effects-based planning, may be structured in a similar way, with operations. Cognitive models may be housed in agent
the goal of assigning resources to missions. architectures, allowing analyzes of emerging scenarios closer
“Planning to achieve an effect” has been used naively as a to the reality and with a clear focus on the command and control
rather straightforward definition of EBP. However, the vast structure.
majority of planners would argue that any previous approach to
D. Capability planning
military planning would include this asseveration [16].
Therefore, it is imperative to clearly define this concept upfront. Is expected from EBP to determine a range of circumstances
that provides degrees of confidence towards the meeting of the
The US Air Force doctrine [2] holds that “there is no single conditions that characterize a desired end state. These
‘effects-based planning’ methodology or process. Rather, operational circumstances have to be linked to the necessary
understanding the principles of an effects-based approach to
capabilities to provide this confidence, not only the necessary
operations should yield certain insights and enhance
means.
comprehension of many general planning concepts”. This is the
reason why it is important to first understand what EBAO E. Empirical information
means. As stated when speaking of the qualitative modeling,
Reference [17] presents the US Joint Forces Command empirical information provided by SMEs is extremely important
definition of EBAO as “a process for obtaining a desired for a successful EBP. In addition to that, information from
strategic outcome or effect on the enemy through the synergistic history, war-gaming, simulations and experiments should be
and cumulative application of the full range of military and strongly pursued so that the complex models can be modelled
nonmilitary capabilities at all levels of conflict”. Another and uncertainties reduced.
definition presented on [1] is that “effects-based operations are
F. Adaptation
operations conceived and planned in a systems framework that
considers the full range of direct, indirect, and cascading effects, The last principle relates to planning for adaptation. Since a
which may—with different degrees of probability—be achieved lot of uncertainties are present and the scenarios may present
by the application of military, diplomatic, psychological, and emergent behavior, it is very important to be able to adapt and
economic instruments”. dynamically change plans even during execution time.
What both of these definitions emphasize is that the process IV. FRAMEWORK
of planning has to be much more intentional on the pursuit of a Before presenting our framework itself, we must first
holistic view of the operation. There is a focus on addressing not provide the necessary context, which is conveyed in Fig. 1. In
only direct physical effects, but several types of indirect effects,
EBAO, effects are defined as results of actions. These actions
which are influenced by each other. Planners are encouraged to
are simply assigned tasks. The ontology described in Section V,
maintain a very broad view of the “big picture”, especially
during execution, not being caught up in details that can tarnish is used to support a matching process between effects and
the end state visualization. resources. The resources in the analyzed scenario are platforms
and sensors, which may be mounted to the platforms or not. The
A better understanding of our approach requires exploring objectives that defined the desired effects are then translated to
EBO’s main concepts, which are described in the next Section. fitness values within the simulation, providing a means for the
However, our framework greatly relies on the EBO principles plans optimization. On the tactical level, these objectives form
listed below, which were suggested by [1]. a specific mission that, on the operational level, leads to the
A. Uncertainty desired end state.
The first principle says that effects-based operations (EBO) ABMS is used to represent this mission, possessing
planners have to rely on methods that explicitly deal with cognition models that encompass the available expert
probabilities and randomness to properly address the inherent
STIDS 2016 Proceedings Page 57
information as well as showing the interaction and coordination During the optimization parameters definition there is a need
between the agents, representing the C4ISR processes involved. of defining a prioritization of these three basic goals. This
With several runs of the simulation scenario, uncertainties can process depends on several factors, such as rules of engagement,
be added mostly on the hostile units’ locations, on available value of the assets, and criticality of the mission. These factors
capabilities, and on different behavior patterns employed. Also, have to be properly valued by the leadership and then
time issues may be initially addressed, since the agents’ parametrized by the analysts to correctly represent the
interactions allow for identifying some of the interferences they commander’s intent (CI) on a top-to-bottom fashion.
generate on each other through the simulation run. At the end, the framework consists of a deeper and more
thorough entity-level simulation with the goal of determining if
the conditions that define the end state are met within a feasible
timeframe by the previously selected best plan. Also, this phase
allows for mission rehearsal and order generation.
To summarize, as extracted from [2] and [19], the right-
hand side terms of Fig. 1 can be individually defined as:
• Resources: all the available assets to generate the desired
effects;
• Tasks: an action or actions that have been assigned to
someone to be performed;
• Actions: result of assigned tasks;
• Effects: all the physical, functional or psychological
outcomes, events or consequences that results from
specific military or nonmilitary actions;
• Objectives: the clearly defined, decisive, and attainable
goals towards which every operation is directed; and
• End state: the set of required conditions that defines
achievement of the commander’s objectives.
As one can notice, the elements presented in the previous
Section are met, since the resources are approached as
Fig. 1. Air operations planning framework.
capabilities and contain several qualitative and empirical
information, which also permeates the other concepts of the
Generating cognition models can be a strenuous process, framework. Uncertainty is handled through simulation layers,
especially when dealing with rule-based scripts for the agent with the ABMS suggestion alongside. Lastly, the design for
behavior definition, which, besides being hard to implement, do adaptation is taken in consideration through the process of
not capture the uncertainties present on military operations. generation of multiple plans, and mostly by the ontological
This is why the approach for the friendly and enemy forces’ reasoning that can quickly change the initial constraints,
threat assessment process relies on probabilistic models, such leading to a faster plan evaluation during dynamic re-planning.
as Bayesian networks, capable of representing the dependencies Each of the last four boxes on the left-hand side of Fig. 1 is
between the entities’ actions and the evidence accrued from the performed by a different software application that are
C4ISR sources available. respectively described as follows:
From the ABMS, each of the generated plans can be A. Semantic Modeling: Protégé
properly simulated and consequently evaluated based on
Protégé is one of the most popular knowledge-modelling
criteria originated from the three superior goals defined by [18]:
environments. It not only allows users to interactively edit
flight safety, combat survival, and mission accomplishment.
knowledge-bases within its graphic user interface, but also
The first goal relates to the need of the pilot to concentrate on
presents a series of plugins that add a number of functionalities
flying the aircraft in a safe way, for instance assuring that it has
and services, such as ontology management tools, multimedia
the necessary amount of fuel, that it flies through proper fly
support, querying and reasoning engines, and problem solving
zones, avoiding collisions with other aircrafts and the terrain.
methods. Also, it has experienced several actualizations in the
The second focuses on the chances of enduring through the
last decades and has a vast user community, featuring high
mission, considering the capabilities of the hostile forces and
stability and usability ratings. As well as the two following
the exposure to them. Lastly, the third goal illustrates the
applications, it is written in Java, allowing for a smoother
original objective of the mission performed, such as gathering
integration in the future ([20], [21], [22]).
intelligence information, striking a ground target, or
suppressing the enemy’s air defenses.
STIDS 2016 Proceedings Page 58
B. Optimization: ECJ various ways. Some of these parts form a large set of utilities
ECJ is a general-purpose evolutionary computation and that has the goal of supporting model design. Finally, as well as
genetic programming framework designed for large, heavy- ECJ, it is developed and maintained by a research group from
George Mason University ([26], [27], [28], [29]).
weight experimental needs. It is a free open-source application
developed by the Department of Computer Science of George D. High-Resolution Simulation: VR-Forces
Mason University. In spite of being more than 10 years old, it VR-Forces is a simulation environment created by VT MÄK
shows great stability and an optimized design, attested by a for scenario generation [30]. The platform is widely used
large number of users in the genetic programming community. throughout the industry, and provides a well-engineered basis
Besides its main goal of attempting to permit as many valid for integrating CGFs with urban, battlefield, maritime, and
combinations as possible of individual representation and airspace activity. Apart from the graphical interface (front-end),
breeding method, fitness and selection procedure, evolutionary VR-Forces consists of a back-end application, which is its actual
algorithm, and parallelism, it contains multi-objective simulation engine. As such, VR-Forces scenarios can be scaled
optimization algorithms, island models, master/slave up by running multiple front-ends and/or back-ends,
evaluation facilities, coevolution, steady-state and evolution communicating through its networking toolkit. Moreover, both
strategies methods, parsimony pressure techniques, and various the VR-Forces front-end and back-end can be extended either by
individual representations ([23], [24], [25]). being embedded into another application or through plug-ins,
using the C++ API provided.
C. Agent-Based Simulation: MASON
Reference [31] provides a study comparing several CGF
MASON is a single-process discrete-event multi-agent simulation software in terms of autonomy, learning and
simulation toolkit written in Java that comprises a fast core adaptation, organization, realism, and architecture. VR-Forces
engine and a fully separated visualization display. It is very was considered to be the most suitable as a development
versatile and easily expandable, providing friendly licensing platform, mostly because its AI capability built-in, very good
options and excellent performance. In addition, it is designed to documentation and technical support, and support for data
support large numbers of agents relatively efficiently on a single logger export. The same conclusion was drawn by [32] in a
machine in models that are entirely encapsulated. Even the much more thorough analysis.
elements of the system itself are highly independent, providing
a modular and consistent way to combine its different parts in
Fig. 2. EBP concepts and relationships.
V. EBO ONTOLOGY This is made very formally, explicitly expressing clear and
precise definitions of concepts and relationships [33]. Besides,
A. Knowledge-base it provides a domain conceptualization of EBAO as expressed
One of the main reasons why this work advocates for the use on Fig. 2, allowing for a better understanding and application of
of ontologies for EBP planning is that they can contain detailed its features.
information about the military domain in a very structured way.
STIDS 2016 Proceedings Page 59
Some of the information contained on our knowledge-base As stated in section VII, our next step is to test this
will be presented on the next section, but Fig. 3 shows a implementations to verify if the limitations of classical
screenshot taken from the proposed ontology on Protégé. It is description logic matchmaking are significant for in this context,
structure from three basic domain concepts: capabilities, sensors generating undesired matching behaviors. If so, other
and platforms. The two latter are the resource descriptions, with methodologies may be embraced, such as the use of
their properties and limitations taken into account. The first, is nonmonotonic formalisms, such as terminological defaults,
related to the actions that can generate the desired effects, but autoepistemic and circumscriptive description logic [35].
also to some of the constraints that can influence the mission, as
discussed in the next section.
After properly modeling the domain with the most
significant parameters and properties, the semantic component
of the framework needs to perform the matchmaking between
resources and the required tasks for effect generation. This calls
for a semantic breakdown of the effects, so that the available
capabilities may be used as generation factors for them. After
that, the matchmaking methods are able to assign the proper
resources as follows.
B. Semantic matchmaking
The notion of matchmaking consists of a procedure to find
correspondences between entities in ontologies [34]. Whereas
process is made by several existing techniques, this work will
focus on a description logic approach as advocated by [35].
First, it is important to define that matchmaking takes place
as a process in which a requester party triggers the mechanism
of finding resources relevant to the request., while the provider
party describes the available resources in advance. With that, the
matchmaking is made through automated analysis and
comparisons of the semantic descriptions of the involved
resources.
For doing so, the entities of an ontology and their
relationships have to be carefully modelled, representing the air
operations domain as a set of concrete resources that vary on
several properties. This variance is intended to allow the
specification of the resources, having different parameters.
However, due to incomplete information, these specifications
not necessarily describe all the parameters completely. To deal
with that, the notions of entailment and satisfiability back the
testing if all request formulas hold in all models of a knowledge
base and if these formulas are logical consequences of it. Fig. 3. Air operations planning framework.
There are several matching infrences that are able to account
for this variance and that can be directly realised by description VI. SCENARIO
logic, ranked in the following way according to their degrees of The proposed scenario represents the definition of a desired
matching [36]: effect yielding intelligence requirements. These requirements
are influenced by several factors including the resource
1) No match: empty intersection between two descriptions;
availability, environment conditions, and hostile activity. Each
2) Intersection: non-empty intersection between two factor imposes restrictions on the matching process. The
descriptions; availability is directly related to the instantiation, the
3) Non-Disjointness: non-empty intersection between two environment produces constraints for some sensors and
descriptions in every possible world; platforms, and the opposing forces impact on the survivability
probabilities as well as on the mission success measurements.
4) Specialisation: subsumption between two descriptions
holds from right to left; To illustrate the EBP focus, the chosen scenario contains the
5) Generalisation: subsumption between two descriptions requirement of an effect of assessing, gaining, and maintaining
air superiority in support of land and maritime schemes of
holds from left to right; and
maneuver, as proposed by [37]. The author already exemplifies
6) Equivalence: subsumption between two descriptions how this effect yields ISR actions, such as: detect, discover, and
holds in both directions. degrade key components of defense systems; confirm damage to
target acquisition radars and height-finding radars.
STIDS 2016 Proceedings Page 60
Fig. 4. Scenario factors of influence.
With these actions in hand, the system needs to take into • Embraer EMB-145 RS (R-99): twinjet remote sensing
consideration the available sensors and platforms to determine aircraft; and
which ones are capable of properly performing them. However,
their characteristics have also to be confronted with the instance • AMX International AMX-R (RA-1): ground-attack
availability, environment conditions and the hostile threats aircraft for reconnaissance.
expected, since this will directly influence the matchmaking
TABLE I. SENSORS ATTACHMENTS TO PLATFORMS
process, as depicted in Fig. 4.
Platform Sensors
A. Sensors
RQ-450 FLIR, SAR
Aircraft mounted sensors present different characteristics
that may also require different flight altitudes and visibilities in P-3AM FLIR, NCES, SAR
order to properly work. Besides, depending on the applications P-95BM NCES, SAR
the image demands may be different, providing alternative
R-99 CES, FLIR, MSS, NCES, OPT, SAR,
information from various sensors. The available sensors
considered in this work are: RA-1 FLIR, OPT
• OPT: Optical sensors; VII. CONCLUSION AND FUTURE WORK
• FLIR: Forward-looking infrared cameras; The main goal of this paper was to provide an analysis of the
problem and a preliminary structure of the framework advocated
• MSS: Multispectral Sensors; to solve it. The work focused on establishing a theoretical basis
• SAR: Synthetic Aperture Radar; for delineating this solution, adapting it to effects-based
approach to operations concepts. An added constraint was to
• NCES: Non-communications exploitation systems; and utilize free and open source applications to form the framework,
at least on its initial phases (the only exception being VR-Forces,
• CES: Communication exploitations systems.
because of the lack of open alternatives that would provide
B. Platforms similar simulation capabilities). Moreover, these applications
As showed in Fig. 4 the considered platforms are some of should be light enough to allow for the execution of the
the aircrafts used by the Brazilian Air Force as ISR assets. These framework on a single machine, what they arguably are.
platforms mount the aforementioned sensors according to The development of the framework not only justifies itself
TABLE I. Besides, each one presents different values for range as being an explicit representation of EBAO, but also on the
and average speed, which may considerably influence the combination of simulation methods with an initial semantic
operations. The aircrafts are: matchmaking process that reduces the solution space, allowing
for a potentially more agile way of determining operational
• Elbit Systems Hermes 450 (RQ-450): medium size
plans. Additionally, the ABMS phase allows for numerous and
unmanned aerial vehicle (UAV);
fast simulation runs, acting as a fitness evaluation tool for the
• Lockheed P-3 Orion (P-3AM): four-engine turboprop optimization process as well as an analyzer of emerging
maritime surveillance aircraft; behaviors and complex C4ISR interactions.
• Embraer EMB-111 Bandeirante Patrulha (P-95BM): At the time of this writing, only the initial implementations
twin-turboprop maritime patrol aircraft; of the ontology described have been performed. Next steps
STIDS 2016 Proceedings Page 61
include the full development and implementation of the The SAM Tool,” presented at the 28th Conference on Computer
matchmaking process. This step is needed so the optimization Communications, Rio de Janeiro, Brazil, 2009.
[14] M. Gomez et al., “An Ontology-Centric Approach to Sensor-Mission
can be executed giving continuity to the proposed methodology. Assignment,” in Knowledge Engineering: Practice and Patterns, A.
Finally, more information regarding the scenario has to be Gangemi and J. Euzenat, Eds. Springer Berlin Heidelberg, 2008, pp.
347–363.
gathered, also allowing for an expansion of its scope, including [15] P. H. Deitz, B. E. Bray, and J. R. Michaelis, “The missions and means
gradually more Air Force related activities, for instance airstrike. framework as an ontology,” 2016, vol. 9831, pp. 983109–983109–12.
[16] B. Bullen, “A Bayesian Methodology for Effects Based Planning,” Dec.
ACKNOWLEDGMENT 2006.
[17] I. Duyvesteyn and J. Angstrom, Modern War and the Utility of Force:
The authors thank Major Breno Leite, from the Brazilian Air
Challenges, Methods and Strategy. Routledge, 2010.
Force, for providing data for the scenario construction as well as [18] A. Schulte, “Cognitive Automation for Tactical Mission Management:
serving as a subject matter expert, contributing with empirical Concept and Prototype Evaluation in Flight Simulator Trials,” Cogn.
information and insights for the modeling. Our gratitude is also Technol. Work, vol. 4, no. 3, pp. 146–159.
extended to Danny Williams, from VT MÄK, who was [19] Joint Staff, “Joint Publication 5-0: Joint Operation Planning,”
instrumental in providing technical expertise in the VR-Forces Washigton, DC, Aug. 2011.
[20] “Protégé.” [Online]. Available: http://protege.stanford.edu/. [Accessed:
suite. 14-Aug-2016].
[21] J. H. Gennari et al., “The evolution of Protégé: an environment for
knowledge-based systems development,” Int. J. Hum.-Comput. Stud.,
vol. 58, no. 1, pp. 89–123, Jan. 2003.
REFERENCES [22] R. Sivakumar and P. V. Arivoli, “Ontology Visualization Protégé Tools
[1] P. K. Davis, Effects-based Operations: A Grand Challenge for the - A Review,” Int. J. Adv. Inf. Technol. IJAIT, vol. 1, no. 4, Aug. 2011.
Analytical Community. RAND, 2001. [23] “ECJ.” [Online]. Available: http://cs.gmu.edu/~eclab/projects/ecj/.
[2] Joint Staff, “ANNEX 3-0 Operations & Planning,” Washigton, DC, [Accessed: 14-Aug-2016].
Aug. 2011. [24] D. R. White, “Software review: the ECJ toolkit,” Genet. Program.
[3] R. Ganger, G. de Mel, T. Pham, R. Rudnicki, and Y. Schreiber, “Sensor Evolvable Mach., vol. 13, no. 1, pp. 65–67, Aug. 2011.
assignment to mission in AI-TECD,” 2016, p. 98310E. [25] S. Luke, “The ECJ Owner’s Manual: A User Manual for the ECJ
[4] B. Rosenberg, M. Richards, J. T. Langton, S. Tenenbaum, and D. W. Evolutionary Computation Library,” George Mason University, Version
Stouch, “Applications of multi-objective evolutionary algorithms to air 23, Jun. 2015.
operations mission planning,” 2008, p. 1879. [26] “MASON Multiagent Simulation Toolkit.” [Online]. Available:
[5] J. P. Ridder and J. C. HandUber, “Mission Planning for Joint http://cs.gmu.edu/~eclab/projects/mason/. [Accessed: 14-Aug-2016].
Suppression of Enemy Air Defenses Using a Genetic Algorithm,” in [27] S. Luke, C. Cioffi-revilla, L. Panait, K. Sullivan, and G. Balan,
Proceedings of the 7th Annual Conference on Genetic and Evolutionary MASON: A Multi-Agent Simulation Environment. .
Computation, New York, NY, USA, 2005, pp. 1929–1936. [28] S. Luke, C. Cioffi-revilla, L. Panait, and K. Sullivan, “MASON: A new
[6] A. Tolk, Ed., Ontology, Epistemology, and Teleology for Modeling and multi-agent simulation toolkit,” in University of Michigan, 2004.
Simulation, vol. 44. Berlin, Heidelberg: Springer Berlin Heidelberg, [29] S. Luke, “Multiagent Simulation And the MASON Library,” George
2013. Mason University, Version 19, Jun. 2015.
[7] J. Qualls and D. J. Russomanno, “Ontological problem-solving [30] “VR-Forces: Computer Generated Forces - VT MÄK.” [Online].
framework for assigning sensor systems and algorithms to high-level Available: http://www.mak.com/products/simulate/vr-forces.
missions,” Sensors, vol. 11, no. 9, pp. 8370–8394, 2011. [Accessed: 14-Aug-2016].
[8] A. Preece, T. Norman, G. de Mel, D. Pizzocaro, M. Sensoy, and T. [31] N. Abdellaoui, A. Taylor, and G. Parkinson, “Comparative Analysis of
Pham, “Agilely Assigning Sensing Assets to Mission Tasks in a Computer Generated Forces’ Artificial Intelligence,” Oct. 2009.
Coalition Context,” IEEE Intell. Syst., vol. 28, no. 1, pp. 57–63, Jan. [32] G. Parkinson, “AI in CGFs Comparative Analysis,” Defence R&D,
2013. Ottawa, Canada, Summary Report, Dec. 2009.
[9] A. Preece et al., “Matching sensors to missions using a knowledge- [33] M. Hadzic, P. Wongthongtham, T. Dillon, and E. Chang, Ontology-
based approach,” 2008, p. 698109. Based Multi-Agent Systems, vol. 219. Berlin, Heidelberg: Springer
[10] A. Preece et al., “Reasoning and resource allocation for sensor-mission Berlin Heidelberg, 2009.
assignment in a coalition context,” in MILCOM 2008 - 2008 IEEE [34] J. Euzenat and P. Shvaiko, Ontology Matching. Secaucus, NJ, USA:
Military Communications Conference, 2008, pp. 1–7. Springer-Verlag New York, Inc., 2007.
[11] G. de Mel, M. Sensoy, W. Vasconcelos, and A. D. Preece, “Flexible [35] S. Grimm, Studies on the Semantic Web : Semantic Matchmaking with
Resource Assignment in Sensor Networks : A Hybrid Reasoning Nonmonotonic Description Logics. Burke, NL: IOS Press, 2009.
Approach,” presented at the 1st International Workshop on the Semantic [36] R. Studer, S. Grimm, and A. Abecker, Semantic Web Services:
Sensor Web (SemSensWeb), Heraklion, Crete, Greece, 2009, pp. 1–15. Concepts, Technologies, and Applications. Springer Science & Business
[12] M. Gomez, A. Preece, and G. de Mel, “Towards Semantic Media, 2007.
Matchmaking in Sensor-Mission Assignment: Analysis of the Missions [37] D. R. Johnson, “Enabling Intelligence, Surveillance, and
and Means Framework,” International Technology Alliance, Technical Reconnaissance Effects for Effects-Based Operations Conditions
Report 1.3, Mar. 2007. (Maxwell Paper, Number 34),” Jun. 2005.
[13] A. Preece, D. Pizzocaro, K. Borowiecki, G. de Mel, M. Gomez, and W.
Vasconcelos, “Sensor Assignment to Missions in a Coalition Context:
STIDS 2016 Proceedings Page 62