=Paper=
{{Paper
|id=Vol-2745/paper2
|storemode=property
|title=In Defence of Design Patterns for AI Planning Knowledge Models (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2745/paper2.pdf
|volume=Vol-2745
|authors=Mauro Vallati,Thomas L. McCluskey
|dblpUrl=https://dblp.org/rec/conf/aiia/VallatiM20
}}
==In Defence of Design Patterns for AI Planning Knowledge Models (short paper)==
https://ceur-ws.org/Vol-2745/paper2.pdf
In Defence of Design Patterns for AI Planning
Knowledge Models?
Mauro Vallati1[0000−0002−8429−3570] and Lee McCluskey1[0000−0001−8181−8127]
University of Huddersfield, Huddersfield, UK
{m.vallati,lee}@hud.ac.uk
Abstract. Design patterns are widely used in various areas of computer
science, the most notable example being software engineering. They have
been introduced also for supporting the encoding of automated planning
knowledge models, but with little success.
In this paper, we argue about the merits of design patterns, as an ex-
ample of the broader class of reusable abstractions, in the automated
planning context; particularly, we aim at drawing attention to their po-
tential usefulness for the explainability of domain-independent planning
systems.
Keywords: Automated Planning · Knowledge Engineering · Design Pat-
terns · Explainability.
1 A Knowledge Engineering (Historical) Perspective
Automated planning has been successfully applied in challenging real-world do-
mains, including drilling [7], transport [16], smart grid [23], UAV control [19],
e-learning [9] and mining [13].
Undoubtedly, the intensive development of domain-independent planning
techniques has contributed to the advancement of planning technology. In domain-
independent planning there is a decoupling between the planning logic, that is
embodied in the planning engine, and the domain knowledge, that comes under
the form of knowledge models. As the two components rely on a well-defined
interface language, they can be substituted by other approaches without any
changes to the rest of the framework.
A critical aspect of domain-independent planning, is the application knowl-
edge that must be added to the planner to create a complete planning applica-
tion. This is made explicit in (i) a domain model, which is a formal representation
of the persistent domain knowledge, and (ii) an associated problem instance, con-
taining the details of the particular problem to be solved. Both these components
are used by automated planning engines for reasoning, in order to synthesise a
solution plan. Formulating knowledge for use in planning engines is currently
?
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
something of an ad-hoc process, where the skills of knowledge engineers signifi-
cantly influence the quality of the resulting planning application [4].
Studies on Knowledge Engineering for Planning and Scheduling (KEPS) have
led to the creation of several tools and techniques to support the design of domain
knowledge structures, and the use of planners for real-world problems [15, 10].
However, tools are still rarely used when experts have to engineer new knowledge
models [4]. It may be due to the fact that experts have to adapt the encoding
and refining processes according to the functionalities of a given tool. Further,
the learning curve can be particularly steep for this kind of tools.
A different yet complementary approach relies on the notion of reusable ab-
stractions (see, for instance, [18]): instead of encoding planning knowledge mod-
els directly in a low-level language such as PDDL, this approach envisages the
use of more abstract level descriptions, that foster the design and use of mod-
ular structures. This would support re-use of similar structures, and provide a
mean for justifying some decisions made during the knowledge acquisition and
encoding processes. One of such reusable abstraction techniques for planning
was proposed in the early 2000s, and leveraged on the notion of design patterns
[20]. Design patterns [1] facilitate reuse of good design practices, as they describe
a recurrent problem and a well-tested solution. Since their introduction, design
patterns have been swiftly and successfully adopted in most areas of computer
science, most notably software engineering, as a mean for fostering reusable and
easy to generalise solutions [8]. The approach introduced by Simpson et al. [20]
proposed a tool for organising and managing patterns, and a way for generating
the corresponding solution either in OCL [14] or in PDDL, to be then incorpo-
rated in knowledge models that can be used by domain-independent planning
engines. Examples of the patterns proposed include a general approach for mov-
ing elements, and to describe structures such as maps, sets, or sequences.
Design patterns remove the mentioned issue of KEPS tools, particularly the
need to learn to use a dedicated tool, and can actively support the widespread
use of best practices in the field. Notably, a rudimentary form of design patterns
is implemented in the well-known online editor planning.domains,1 as the “Misc
PDDL Generators” plugin. This plugin provides the PDDL code for representing
some common structures, such as grids and maps. There is not, however, a
description of the problem that such structures are aimed at solving, and there
is no justification for the specific encoding that is suggested.
Unfortunately, aside from the mentioned planning.domains example, design
patterns are not widely used by the AI planning community. A reason for that
may be the lack of a centralised repository for managing such design patterns,
the lack of a commonly agreed way for encoding them, and the fact that the
encoding of planning knowledge models is still seen as a sort of ad-hoc arti-
san process, whose shortcomings can be dealt with by using high-performance
planning engines rather than a proper engineering process.
1
http://editor.planning.domains/
2 Beyond Knowledge Encoding: Explainability
Here we would like to highlight the important role that reusable abstractions,
and particularly design patterns can play for AI planning knowledge models,
beside the support for knowledge encoding and acquisition.
An area of growing importance for the automated planning field, as well as for
AI in general, is that of explainability. The idea being that an AI system should
be able to explain, to some extent, its behaviour to stakeholders. Focusing on
the narrower topic dubbed XAIP, as for EXplainable AI Planning, a number of
recent works considered the problem of defining what “explainable” means for an
automated planning system, explaining and describing generated plans, bridging
the gap between machines and human stakeholders, and designing approaches
to explain the behaviour of planning systems [6, 5, 3, 22, 21, 2].
As noted in the previous section, domain-independent planning systems are
heavily dependent on the provided knowledge models; this is also true when ex-
plainability is concerned. The behaviour of a planning system can not transcend
the knowledge model, and the importance of the planning knowledge model has
been well-argued [17]. In fact, in the XAIP field, the knowledge model is more
and more regarded as a source of knowledge that can explain the behaviour of
the planning system [24, 11]. This is also because, during the knowledge engineer-
ing process of encoding specifications into an appropriate planning knowledge
model a number of design decisions have to be made. Some of those decisions are
due to the experience of the encoder, while others are due to either common or
specific knowledge about the application context. Clearly, other aspects of the
knowledge model support explainability also: the fact that dynamic knowledge
is stated with both a procedural and declarative interpretation means that parts
of the knowledge can be used in isolation within an explanation. Even namings
within the model can be used as an understandable unit of the explanation.
It should therefore come as no surprise that design patterns can play a sig-
nificant role in the XAIP field. Design patterns can provide a valuable and
well-defined mean for supporting explanations. This is because, when design
patterns are implemented in a planning model, the design pattern description
becomes a valuable source of additional knowledge, that is not encoded in the
model. Further, there is not even the need for finding a way to “attach” such
additional knowledge to the standard planning models. The management of ad-
ditional knowledge is a potential issue for planning systems [24]. Instead, having
a centralised repository for planning design patterns (something similar to what
Source Making 2 does for software engineering, for instance) would allow plan-
ning models to be encoded and circulated as they are right now, and would
support explanation engines in looking for patterns and descriptions in a struc-
tured manner. In fact, the XAIP field can provide new momentum to the use
and management of design patterns for planning.
In a sense, the use of patterns becomes then an explicit way for providing
explanations, that can of course be complemented by the line of work that de-
2
https://sourcemaking.com/
Fig. 1: The Mobile design pattern, described via a dedicated interface (Pictures
taken from [20]).
vises techniques for identifying known substructures of planning models, and
link them to an expected behaviour (see for instance [12]). It is naive to believe
that a complex planning model can be composed only by design patterns, but
at least the core elements are expected to be encoded using some form of pat-
terns. Additional sources of domain-specific knowledge can then be sought via
ontologies or similar structures [24].
3 Example: The Mobile Design Pattern
For exemplifying our vision for the use of design patterns for explainability,
let us consider the Mobile design pattern introduced by Simpson et al. [20],
and partly shown in Figure 1. The Mobile pattern deals with the problem of
encoding mobile elements (typically vehicles) in the models, that can be used to
carry goods or people, and may require a driver to be moved.
The problem description, the use of appropriate naming convention, and the
optional components can allow to deal with the classes of explanations that
Vallati, McCluskey and Chrpa defined as context-related and assumption-related
[18]. The former class refers to explanations that relate to contextual knowledge
about the domains, the latter class requires information about assumptions and
decisions made during the knowledge encoding process.
A context-related question that can be answered using knowledge that comes
with the Mobile design pattern would be: Why can the vehicle not move from
location X to location Y? The pattern description includes the answer to this
question, by stating that the movement can be allowed between adjacent loca-
tions in a map. Similarly, taking into account the optional components, the use
of the design pattern can help to answer questions like Why did the vehicle not
move immediately at the start of the plan? Here the fact that a vehicle needs a
driver to move can help in addressing this question. Assumptions-related ques-
tions can then relate to the maximum capacity of the vehicle, for instance, or to
the fuel consumption of the vehicle.
Given the provided example, it should be clear that to exploit design patterns
for supporting explainability, there is the need for structuring the way in which
the corresponding problem description, optional components, etc. are encoded
in a repository, and for dedicated approaches able to identify the most relevant
knowledge with regards to a given query. However, the planning community can
take inspiration from the work done in other research areas, for instance Knowl-
edge Engineering and Knowledge Management, that have decades of experience
in addressing similar challenges.
4 Conclusion
Reusable abstractions, mostly under the form of design patterns, have been
introduced for supporting the knowledge encoding and acquisition processes.
However, they did not have great success in the KEPS field.
In this paper, we point to the fact that design patterns can play a signifi-
cant role also for supporting the explainability of the behaviour of a planning
system. The benefit for Knowledge engineers derived by using design patterns in
planning models can therefore be twofold: (i) design patterns allow to deal with
common problems by reusing best practices, and (ii) they directly support the
explanaibility of the knowledge models and, indirectly, of the overall planning
system.
Future work is envisaged in identifying suitable way for storing and managing
design patterns for automated planning, with focus given to the way in which the
problem they address is described in an explainable-friendly manner, and to the
planning languages that are supported. A general repository of design patterns
for the planning community, as it is currently done for benchmarks, could foster
their use and give momentum to this important area of research.
References
1. Alexander, C.: A pattern language: towns, buildings, construction. Oxford univer-
sity press (1977)
2. Caminada, M.W., Kutlak, R., Oren, N., Vasconcelos, W.W.: Scrutable plan enact-
ment via argumentation and natural language generation. In: Proceedings of the
2014 international conference on Autonomous agents and multi-agent systems. pp.
1625–1626 (2014)
3. Chakraborti, T., Kulkarni, A., Sreedharan, S., Smith, D.E., Kambhampati, S.: Ex-
plicability? legibility? predictability? transparency? privacy? security? the emerg-
ing landscape of interpretable agent behavior. In: Proceedings of the Twenty-Ninth
International Conference on Automated Planning and Scheduling, ICAPS. pp. 86–
96 (2019)
4. Chrpa, L., McCluskey, T.L., Vallati, M., Vaquero, T.S.: The fifth international
competition on knowledge engineering for planning and scheduling: Summary and
trends. AI Magazine 38(1), 104–106 (2017)
5. Eifler, R., Cashmore, M., Hoffmann, J., Magazzeni, D., Steinmetz, M.: A new ap-
proach to plan-space explanation: Analyzing plan-property dependencies in over-
subscription planning. In: The Thirty-Fourth AAAI Conference on Artificial Intel-
ligence, AAAI. pp. 9818–9826 (2020)
6. Fox, M., Long, D., Magazzeni, D.: Explainable planning. CoRR abs/1709.10256
(2017)
7. Fox, M., Long, D., Tamboise, G., Isangulov, R.: Creating and executing a well
construction/operation plan (2018), uS Patent App. 15/541,381
8. Gamma, E., Helm, R., Vlissides, J., Johnson, R.: Design Patterns Elements of
Reusable Object-Oriented Software. Addison-Wesley (1997)
9. Garrido, A., Morales, L., Serina, I.: Using AI planning to enhance e-learning pro-
cesses. In: Proceedings of the Twenty-Second International Conference on Auto-
mated Planning and Scheduling, ICAPS. AAAI (2012)
10. Jilani, R., Crampton, A., Kitchin, D., Vallati, M.: Automated knowledge engineer-
ing tools in planning: State-of-the-art and future challenges. In: Proceedings of
KEPS (2014)
11. Lindsay, A.: Towards exploiting generic problem structures in explanations for au-
tomated planning. In: Proceedings of the 10th International Conference on Knowl-
edge Capture, K-CAP. pp. 235–238 (2019)
12. Lindsay, A.: Using generic subproblems for understanding and answering queries
in xaip. In: Proceedings of KEPS (2020)
13. Lipovetzky, N., Burt, C.N., Pearce, A.R., Stuckey, P.J.: Planning for mining op-
erations with time and resource constraints. In: Proceedings of the International
Conference on Automated Planning and Scheduling (2014)
14. Liu, D., McCluskey, T.: The ocl language manual. Tech. rep., Version 1.2. Technical
report, Department of Computing and Mathematical . . . (2000)
15. McCluskey, T.L., Aler, R., Borrajo, D., Garagnani, M., Haslum, P., Jarvis,
P., Refanidis, I., Scholz, U.: Knowledge Engineering for Planning Roadmap.
http://scom.hud.ac.uk/planet/home (2003)
16. McCluskey, T.L., Vallati, M.: Embedding automated planning within urban traf-
fic management operations. In: Proceedings of the International Conference on
Automated Planning and Scheduling (2017)
17. McCluskey, T.L., Vaquero, T.S., Vallati, M.: Engineering knowledge for automated
planning: Towards a notion of quality. In: Proceedings of the Knowledge Capture
Conference, K-CAP. pp. 14:1–14:8 (2017)
18. McCluskey, T., Simpson, R.: Towards an algebraic formulation of domain defini-
tions using parameterised machines. In: Proceedings of the Annual UK PLANSIG
Workshop (2005)
19. Ramı́rez, M., Papasimeon, M., Lipovetzky, N., Benke, L., Miller, T., Pearce, A.R.,
Scala, E., Zamani, M.: Integrated hybrid planning and programmed control for
real time UAV maneuvering. In: Proceedings of the 17th International Conference
on Autonomous Agents and MultiAgent Systems, AAMAS. pp. 1318–1326 (2018)
20. Simpson, R., Mccluskey, T., Long, D., Fox, M.: Generic types as design patterns for
planning domain specification. In: AIPS2002 Workshop on Knowledge Engineering
Tools and Techniques for AI Planning (2002)
21. Sohrabi, S., Baier, J.A., McIlraith, S.A.: Preferred explanations: Theory and gen-
eration via planning. In: Twenty-Fifth AAAI Conference on Artificial Intelligence
(2011)
22. Sreedharan, S., Srivastava, S., Smith, D., Kambhampati, S.: Why can’t you do that
hal? explaining unsolvability of planning tasks. In: International Joint Conference
on Artificial Intelligence (2019)
23. Thiébaux, S., Coffrin, C., Hijazi, H., Slaney, J.: Planning with mip for supply
restoration in power distribution systems. In: Proceedings of the International Joint
Conference on Artificial Intelligence (2013)
24. Vallati, M., McCluskey, L., Chrpa, L.: Towards explanation-supportive knowledge
engineering for planning. In: Proceedings of XAIP (2018)