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ltlfsynt

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Reactive synthesis is the problem of synthesizing a controller that produces output signals based on a history of input signals, under the constraint of some specification that relates the input and output signals over time.

ltlfsynt solves this problem when the specification is an LTLf formula (i.e., LTL interpreted over finite traces). It can either decide realizability (i.e., decide if a controller satisfying the formula exist) or synthesize a reactive controller as an And-Inverter Graph (AIG).

Consider a set \(I\) of input atomic propositions, a set \(O\) of output atomic propositions, and an LTLf formula φ over the propositions in \(I \cup O\). This tool can build controllers using two different semantics:

Mealy controller
A Mealy controller is a function \(c: (2^{I})^+\to 2^O\). A Mealy controller realizes \(\varphi\) if for any ω-word \((u_i)_{i \in \mathbb{N}} \in (2^I)^\omega\) over the input propositions, there exists some position \(k>0\) such that the (nonempty) word \((u_i \cup c(u_0 \dots u_{i-1}, u_i))_{0 \le i \le k}\in (2^{I\cup O})^k\) satisfies φ.
Moore controller
A Moore controller is a function \(c: (2^{I})^*\to 2^O\). A Moore controller realizes \(\varphi\) if for any ω-word \((u_i)_{i \in \mathbb{N}} \in (2^I)^\omega\) over the input propositions, there exists some position \(k>0\) such that the (nonempty) word \((u_i \cup c(u_0 \dots u_{i-1}))_{0 \le i \le k}\in (2^{I\cup O})^k\) satisfies φ.

In other words, a Mealy controller should decide the outputs for the current instant based on the history of all inputs as well as the current inputs. A Moore controller should decide outputs without knowing the current inputs, it only knows about past inputs. In either semantics, the controller should ensure that φ is satisfied after a finite number of steps. Once φ has been satisfied, the output of the controller is unconstrained.

Example

Here is an example LTLf specification generated by genltl:

f=`genltl --tv-counter-mealy=2`
echo "$f" | sed 's/(*G/\n&/g'
(init0 <-> ob0) & (init1 <-> ob1) & 
G(inc <-> oc0) & 
G((X[!]ob0 -> (ob0 xor oc0)) & (X[!]!ob0 -> (ob0 <-> oc0))) & 
G(oc1 <-> (ob0 & oc0)) & 
G((X[!]ob1 -> (ob1 xor oc1)) & (X[!]!ob1 -> (ob1 <-> oc1))) & 
(G(!inc -> X[!]inc) -> F(!ob0 & !ob1))

Output variables ob0 and ob1 encode a 2-bit counter. Input variables init0 and init1 specify the initial value of this counter, as encoded on the first line.

When the input variable inc is set, the counter should be incremented by one on the next step. This is encoded on the next four lines, by using to extra output variables to represent carries (oc0 and oc1), and ensuring that, if a next step exist, the next value of the counter (X[!]ob0 and X[!]ob1) will be set according to its current value and to that of the carries.

X[!] is Spot's notation for the strong next operator of LTLf. The weak next is simply noted X. Since we are working with finite words, a strong next ensure that the current instant is not the last one. A weak next is always satisfied on the last instant.

The last implication is something we would like to confirm under the above rules, and regardless of the initial value of the counter. Assuming the input variable inc is raised at least every other step (G(!inc -> X[!]inc)), we claim that the counter will eventually wrap back to zero F(!ob0 & !ob1).

We show that the answer is positive by passing this formula to ltlfsynt. (Notice that in this formula, input and output variables start with i and o respectively, so ltlfsynt can tell them apart automatically.)

ltlfsynt --semantic=Mealy -f "$f" --realizability
REALIZABLE

This specification is realizable.

Without the --realizability switch, ltlfsynt will additionally print a Mealy machine representing the controller, in the HOA format.

ltlfsynt --semantic=Mealy -f "$f"
REALIZABLE
HOA: v1
States: 5
Start: 0
AP: 7 "init0" "init1" "inc" "ob0" "ob1" "oc1" "oc0"
acc-name: all
Acceptance: 0 t
properties: trans-labels explicit-labels state-acc deterministic
controllable-AP: 3 4 5 6
--BODY--
State: 0
[!0&!1&!2&!3&!4&!5&!6 | !0&!1&2&!3&!4&!5&6 | !0&1&!2&!3&4&!5&!6 | 0&!1&!2&3&!4&!5&!6 | 0&1&!2&3&4&!5&!6] 1
[!0&1&2&!3&4&!5&6] 2
[0&!1&2&3&!4&5&6] 3
[0&1&2&3&4&5&6] 4
State: 1
[!2&!6 | 2&6] 1
State: 2
[!2&3&4&!5&!6] 1
[2&3&4&5&6] 4
State: 3
[!2&!3&4&!5&!6] 1
[2&!3&4&!5&6] 2
State: 4
[!2&!3&!4&!5&!6 | 2&!3&!4&!5&6] 1
--END--

We can hide the REALIZABLE line with --hide, and print the Mealy machine in GraphViz's format as follows:

ltlfsynt --semantic=Mealy -f "$f" --hide --dot=strategy

Sorry, your browser does not support SVG.

Remember that unlike ltlsynt, we are working with finite semantics here. The controller will read a sequence of input assignments of infinite size, but that sequence should have at least one position such that the combination of inputs and outputs up to that point satisfy the specification. Once the specification has been satisfied, the controller is free to output anything. This is why the values of ob0, ob1, oc1 are not specified on the transition leaving the rightmost state: that state is reached either when the counter wrapped to zero, or when the input signal do not satisfy our assumption (G(!inc -> X[!]inc)), so at that point the specification has been satisfied and the output variable can be chosen freely.

The reason we still force the value of oc0 to be equal to that of inc on the last state is because of an optimization in ltlfsynt: since the specification contains G(inc <-> oc0), the formula can be simplified by replacing all occurrences of oc0 by inc. ltlfsynt does this to reduce the number of variables it has to deal with, and only adds the oc0 variable back at the very end, before presenting results.

The controller may be output as an And-Inverter-Graph in AIGER format using the --aiger option:

ltlfsynt --semantic=Mealy -f "$f" --aiger
REALIZABLE
aag 59 3 3 4 53
2
4
6
8 83
10 109
12 119
25
35
45
6
14 10 13
16 9 14
18 9 13
20 2 18
22 3 16
24 21 23
26 8 14
28 19 27
30 4 29
32 5 14
34 31 33
36 6 16
38 6 18
40 2 38
42 3 36
44 41 43
46 10 12
48 8 13
50 9 47
52 49 51
54 11 13
56 8 54
58 9 11
60 57 59
62 6 61
64 7 53
66 63 65
68 11 12
70 9 68
72 57 71
74 6 73
76 65 75
78 4 77
80 5 67
82 79 81
84 6 26
86 9 54
88 27 87
90 6 89
92 4 90
94 5 84
96 93 95
98 4 84
100 5 90
102 99 101
104 2 103
106 3 97
108 105 107
110 4 38
112 5 36
114 111 113
116 2 115
118 43 117
i0 init0
i1 init1
i2 inc
o0 ob0
o1 ob1
o2 oc1
o3 oc0

Here is a graphical representation of the And-Inverter Graph for this controller:

ltlfsynt --semantic=Mealy -f "$f" --hide --dot=aig

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The input signals are shown at the bottom in blue triangles. The output signals are shown at that top in red triangles. Three latches (1-bit registers), represented with orange rectangles are used to remember the state of the controller. Latches are all false initially. The L*_out nodes emit the current latch value, and the L*_in receive the value of the latch for the next step. All round nodes correspond to binary AND gates. Black dots invert a signal.

Invoking ltlfsynt

The specification can be supplied in three ways:

  • as an LTLf formula, supplied on the command-line: -f formula.
  • as an LTLf formula, stored in a file: -F FILENAME.
  • as a file in the TLSF v1.2 format: --tlsf FILENAME.

When receiving an LTLf formula with -f or -F, ltlfilt needs to know which atomic propositions are input or output. This can be specified with options --ins, --outs, or with --part-file. See ltlfsynt's documentation of those options. Without any such option, ltlfsynt assumes that variables starting with i are inputs, and those starting with o are output.

Similarly, the semantics also have to be specified using --semantics=Mealy (the default) or --semantics=Moore. When comparing ltlfsynt to other tools, beware that they may have not have the same semantics by default.

When the specification is passed as a TLSF file with the --tlsf option, the syfco tool is automatically invoked to transform the specification into LTLf, to retrieve the lists of input/output variables, and to obtain the semantics to use.

Fine-tuning

ltlfsynt operates in several phases as follows:

  1. preprocess the specification
  2. translate the LTLf specification into two-player game
  3. solve the two-player game
  4. extract a winning strategy as a Mealy machine
  5. encode the Mealy machine in AIG

Steps 2 & 3 are optionally merged together.

Preprocessing

Various optimizations are enabled by default.

  • Variables that always appear with the same polarity in the specification can be changed into constants and removed from the specification. This optimization can be disabled with --polarity=no.
  • Group of variables that are specified as equivalent can be replaced by a unique variable of that group. This can be disabled with --global-equivalence=no.
  • The specification can be decomposed into multiple output-disjoint specifications that can be solved independently. This decomposition can be disabled with --decompose=no.
  • The formula is simplified with some cheap rewriting rules. This is especially important when decomposition is used, because the decomposition sometimes need to distribute subformulas to favor decompositions. This can be disabled with --simplify=no.

Translation & Game solving

By default those two steps are combined and performed on-the-fly. This gives the best results. ltlfilt supports alternative approaches. In all approaches, the DFA that is constructed is represented using multi-terminal binary decision diagrams (MTBDD).

Off-line approaches

In these approaches, first a DFA is built, then it is interpreted as a game and solved. The why the DFA is built is controlled by the --translation option:

  • --translation=full: a full DFA is created for the input specification; this is a direct translation from LTLf to MTBDD-based DFA.
  • --translation=compositional: a full DFA is created for the input specification, but the translation is compositional. The specification will be split on top-level Boolean operators, then subformulas will be translated separately, and finally the resulting automata will be combined.
  • --translation=restricted builds a restricted version of the DFA in which accepting states are replaced by accepting sinks. This is a direct translation as well.

The restricted translation is always faster than the full translation. The compositional translation is usually faster than the full translation as well (provided intermediate automata are minimized). restricted is usually better than compositional on meaningful specifications, and compositional is often than restricted or specifications built from random combination specifications.

The automaton that is built can be minimized. This is controlled with the --minimize=yes and --minimize=no options. By default, minimization is only turned on for --translation=compositional, because minimization is what makes the compositional translation fast. For other approaches, minimization is not recommended, because solving the unminimized automaton is faster than minimizing it.

The MTBDD-based DFA is interpreted as game that can be solved in two ways:

  • --backprop=nodes: solve the game by backpropagation at the level of the MTBDD nodes. This is a linear algorithm.
  • --backprop=states: solve the game by backpropagation but at the level of the MTDFA states. This uses less memory, but the algorithm may require quadratic time.

On-the-fly approaches

The DFA can be built on-the-fly. Thiss build the equivalent of the "restricted" automaton from above and solve the game as it is constructed.

The order in which states are explored can be controlled with --translation=dfs or --translation=bfs.

The DFS on-the-fly translation is the default. Compared to the BFS, it allows an extra optimization: when a state is backtracked, we know that all its successors have been explored, so if it has not been decided that the state was winning, we can mark it as losing (and this information can be backpropagated).

An extra optimization that is enabled by default in on-the-fly approaches is one-step (un)realizability where the formula α for the current step is rewritten into two formulas Boolean formulas \(\alpha_r\) and \(\alpha_u\) such that if \(\alpha_r\) is realizable then \(\alpha\) is realizable, and if \(\alpha_u\) is unrealizable, then \(\alpha\) is unrealizable. There are some specifications where this optimization is a huge help. Conversely, for some specifications where this does not help, this is just an unnecessary overhead. You may disable it with --one-step-preprocess=no.

Realizability

Use the --realizability to interrupt the process once the game has been solved.