There are a number of ways in which this work can be extended. These constitute interesting directions for future research.
A critical form of behaviour adaptation stems from the application of machine learning techniques. These have not been explored in this thesis. There are a few areas where machine learning techniques could be useful, such as to replace the heuristic function (e.g. Equation 4.1 and Equation 7.1), to replace the environment behaviour
model in Chapter 8, or even the state update operator. The challenges of utilising machine learning techniques may include having large amount of past instances with annotated outcome values as well as the historical behaviour of the environment.
Additionally, any statistical model may be only applicable to a very specific domain (e.g. for a specific process or environment). With more and more operational data collected in businesses, it is possible, in near future, that there are enough detailed data to support building machine learning or deep learning models.
There are a range of issue relating to game-tree search that have not been explored in this thesis. These include the use of other game-tree search techniques, probabilistic game-tree search, other variants of MCTS and so on. In the case of minimax search and its variants, the use of machine learning techniques in computing the evaluation function have not been explored.
The question of merging business process models (sometimes referred to as the process integration problem), in a manner similar to the merging of agent programs has not been explored in this thesis. However, the chapter on merging agent programs provides enough pointers to make this a potentially easy exercise.
Other possible formal notations for states and state update operators are not explored. The semantics of agent programs and processes are described using propositional logic. The extensions to first-order logic, default logic, and temporal logic will offer much greater expressiveness in different application domains. However, such extensions also increases the computation complexity, and potentially makes the approach less practical in runtime. The use of different formal languages also results in changing the state update operator and its computation complexity.
Other future research directions may include behaviour understanding (intention mining/recognition), discovering new behaviour and detecting undesirable behaviour in autonomous system, preventing autonomous system from reaching undesirable state,
etc.
Process Models
Process01
Model
Capability Library
T01 {a}
T02 {b, c}
T03 {¬b}
T04 {¬c}
Knowledge Base
a ∧ ¬b → c 170
b → a c → a
Process02
Model
Capability Library
T01 {a, b}
T02 {¬b, ¬d}
T03 {¬a, c}
T04 {d}
Knowledge Base
a ∧ ¬b → c c ∧ ¬d → b
Process03
Model
Capability Library
T01 {a, b, c, d, e, f, g}
T02 {¬b, ¬d, ¬f } T03 {¬a, ¬c, ¬e, ¬g}
T04 {b, d, f } T05 {a, c, e, g}
T06 {¬b}
T07 {¬f, ¬g}
T08 {¬a, ¬d}
T09 {¬c}
T10 {b, c}
Knowledge Base
a ∧ ¬b → c c ∧ ¬d → e e ∧ ¬f → g
Process04
Model
Capability Library
T01 {a, b, c, d}
T02 {¬b, ¬d, ¬f, i}
T03 {¬a, ¬c, ¬e}
T04 {b, d, f, ¬h}
T05 {a, c, e, g}
T06 {h, j, ¬l}
T07 {k, m}
Knowledge Base
a ∧ ¬b → c c ∧ ¬d → e e ∧ ¬f → g
¬b → a
¬d → a h → ¬a
¬j → ¬a
Process05
Model
Capability Library
T01 {a, b, c, d, e, f, g}
T02 {¬b, ¬d, ¬f, i}
T03 {¬a, ¬c, ¬e, ¬g, ¬i, j, k}
T04 {b, d, f, ¬h, ¬j, l, m}
T05 {a, c, e, g, i, ¬k, ¬m}
T06 {h, j, ¬l}
T07 {k, m}
Knowledge Base
a ∧ ¬b → c c ∧ ¬d → e e ∧ ¬f → g
¬b → a
¬d → a h → ¬a
¬j → ¬a
Process06
Model
Capability Library
T01 {a}
T02 {c, d}
T03 {¬c, d}
T04 {¬d}
T05 {¬a, c}
T06 {¬c, d, ¬e}
T07 {e}
T08 {¬b, e}
T09 {¬a}
Knowledge Base
a ∧ ¬b → c
¬a → e ∨ d
Process07
Model
Capability Library
T01 {a, d, e, f, g}
T02 {¬f, h, i}
T03 {¬a, ¬c, ¬e, ¬g, ¬i, j, k}
T04 {¬d, f, ¬h, ¬j, l, m}
T05 {a, c, e, g, i, ¬k, ¬m}
T06 {¬b, h, j, ¬l}
T07 {k, m}
T08 {l}
T09 {¬b, ¬k, ¬m}
T10 {b, c, ¬d}
Knowledge Base
a ∧ ¬b ∧ ¬m → c c ∧ ¬d → e e ∧ ¬f → g
¬b ∨ ¬d → a h ∧ ¬j → ¬a l → k ∨ m
Process08
Model
The process (simplified) for an Australian student visa application.
Capability Library
T01 {level1}, {level2}, {level3}, {level4}
T02 {doc01, doc02, doc03, doc04, doc05, doc06}
T03 {doc08, doc09, doc10, doc11}
T04 {doc07}
T05 {onlineApplicationF ormCompleted¬k, ¬m}
T06 {f eeP aidOnline}
T07 {157AF ormCompleted}
T08 {creditCardP aymentAuthorized}
T09 {creditCardP aymentAuthorized}
T10 {healthExaminationBooked}
Knowledge Base
level1 → ¬level2 ∧ ¬level3 ∧ ¬level4 level2 → ¬level1 ∧ ¬level3 ∧ ¬level4 level3 → ¬level1 ∧ ¬level2 ∧ ¬level4 level4 → ¬level1 ∧ ¬level2 ∧ ¬level3
¬level2 ∧ ¬level3 ∧ ¬level4 → level1
¬level1 ∧ ¬level3 ∧ ¬level4 → level2
¬level1 ∧ ¬level2 ∧ ¬level4 → level3
¬level1 ∧ ¬level2 ∧ ¬level3 → level4 online → ¬of f line
of f line → ¬online
of f line → inP erson ∨ byP ost
Process09
Model
The “Customer On Boarding” example process from http://workflowpatterns.
com/ (accessed on 2014).
Capability Library
T01 {requested}
T02 {available}, {¬available}
T03 {rejected}
T04 {inf oChecked}
T05 {valid}, {¬valid}
T06 {open}
T07 {activate}
Knowledge Base
[empty]
The knowledge base is empty because the effect of the tasks and the domain knowledge do not require any extra rules in the knowledge base.
Process10
Model
Capability Library
T01 {invoiceIn}
T02 {invoiceChecked}
T03 {newEntryCreated}
T04 {invoiceDetailInserted}
T05 {customerDetailInserted}
T06 {mismatchChecked}
T07 {invoiceBlocked}
T08 {invoiceOut}
Knowledge Base
[empty]
The knowledge base is empty because the effect of the tasks and the domain knowledge do not require any extra rules in the knowledge base.
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