Temporal Probabilistic Inference Examples

Below we provide example MLNs that demonstrate some LoMRF advanced inference capabilities in the domain of temporal reasoning.

Yale Shooting Scenario

For a quick introduction to temporal representation (using the Probabilistic Event Calculus formalism, see Skarlatidis et. al. (2011, 2014, 2015)) and reasoning, see the temporal reasoning example in Quick Start section.

Activity Recognition

In this example we demonstrate how to perform probabilistic activity recognition, using a small fragment of the first set of the CAVIAR dataset. We use the same Probabilistic Event Calculus formalism as presented in the Quick Start section.

The aim in activity recognition is to recognize complex activities that take place between multiple persons, by exploiting information about simple observed individual activities. The first set CAVIAR dataset comprises of 28 surveillance videos, where each frame is annotated by human experts from the CAVIAR team on two levels. The first level contains simple, input events that concern activities of individual persons or the state of objects. The second level contains composite event annotations, describing the activities between multiple persons and/or objects - i.e., people meeting and moving together, leaving an object and fighting.

Below we briefly describe the input of the activity recognition example:

  1. The input is composed of a collection of input events, representing people walking, running, staying active, or inactive. The first and the last time that a person or an object was tracked are represented by the input events enter and exit. Additionally, the coordinates of tracked persons or objects are also taken into consideration, in order to express qualitative spatial relations, e.g., two persons being relatively close to each other.

  2. In the following sub-section we give the composite event definitions of meeting, moving, leaving an object and fighting. These definitions take the form of common sense rules and describe the conditions under which a composite event starts or ends. For example, when two persons are walking together with the same orientation, then moving starts being recognized. Similarly, when the same persons walk away from each other, then moving stops being recognized.

Based on a collection of input events and composite event definitions, we would like to perform probabilistic inference and recognize instances of the composite events of interest.

Domain types

In this application we have the following domain types:

Domain type Description
id domain of constants that uniquely identify persons, and possibly objects, that appear in the scene, e.g., the constants ID1, ID2, etc.
dist domain of numeric thresholds, that indicate relative distances between persons and/or objects in the scene.
time domain of time, representing the video frame number.
events domain of constants that uniquely identify the occurrence of input events from persons and objects in the scene.
fluents domain of constants that uniquely identify the recognition of output composite events between pairs of persons and/or objects in the scene.

Function schemas

Input events occur over single person/objects that appear in the scene. We represent them in LoMRF as term functions (see Function Definitions section).

Input Events Description
walking(x) person x is walking
running(x) person x is running
active(x) person x is active, e.g., moving her arms, while staying at the same position
inactive(x) person x is inactive, i.e., standing still
enter(x) person x enters the scene
exit(x) person x exits from the scene

In particular, the schema of term functions that represent the input events is given below:

event walking(id)
event running(id)
event active(id)
event inactive(id)
event enter(id)
event exit(id)

Output composite events are defined over pairs of person/objects that appear in the scene. We represent them in LoMRF as term functions (see Function Definitions in Syntax section).

Output Composite Events Description
move(x,y) persons x and y are moving together
meet(x,y) persons x and y are meeting
fight(x,y) persons x and y are fighting
leaving_object(x,y) person x is leaving a object y

The schema of term functions that represent the output composite events is given below:

fluent move(id,id)
fluent meet(id,id)
fluent fight(id,id)
fluent leaving_object(id,id)

Predicate schemas

The predicates that we use follow the definition of the Probabilistic Event Calculus (Skarlatidis et. al. (2011, 2014, 2015)) formalism. We additionally use some utility predicates in order to represent some spacial constraints.

Predicate Meaning
Happens(e, t) An input event e occurs at some point at time t
HoldsAt(f, t) An output composite event f holds at some point at time t
InitiatedAt(f, t) An output composite event f is initiated at some point at time t, i.e., begins to hold
TerminatedAt(f, t) An output composite event f is terminated at some point at time t, i.e., stops to hold
Close(x,y,d,t) The relative distance between the persons/objects x and y is below the threshold d at time t
OrientationMove(x,y,t) Persons x and y have similar orientation at time t
StartTime(t) Utility predicate, stating the beginning of time. We use it to represent the initial state of the example

The schema of the predicates that we use in this example it defined in LoMRF as below:

Happens(event, time)
HoldsAt(fluent, time)
InitiatedAt(fluent, time)
TerminatedAt(fluent, time)
Close(id, id, dist, time)
OrientationMove(id, id, time)

Domain-independent definitions of the Probabilistic Event Calculus formalism

So far we have defined the domain types, as well as the predicate and term function schemas. Below we define the domain-independent axioms of the probabilistic Event Calculus formalism.

InitiatedAt(f, t) => HoldsAt(f, t++).

TerminatedAt(f, t) => !HoldsAt(f, t++).

HoldsAt(f, t) ^ !TerminatedAt(f, t) => HoldsAt(f, t++).

!HoldsAt(f, t) ^ !InitiatedAt(f, t) => !HoldsAt(f, t++).

For details see the Domain-independent axioms sub-section in Quick Start.

We additionally add the following formula, in order to state that at the beginning of time nothing holds:

// initially nothing holds:
StartTime(t) => !HoldsAt(f, t).

Domain-dependent definitions

Below we give the definitions of composite events that express long-term behavior patterns of interest. We assume that we know the weight values (e.g., expert knowledge or we have performed weight learning). For simplicity and compactness, we only present the definitions of moving and meeting.

People are moving together

According to the definitions below, moving composite event is initiated when two persons p1 and p2 are walking close to each other (their distance is at most 34 pixels) with almost the same orientation. The composite event is terminated under several cases: (a) When people walk away from each other, i.e., they have a distance larger than 34 pixels, (b) when they stop moving, i.e., either both are active, or one is active while the other is inactive. (c) When one of them is running or exiting the scene.

// When people begin to move together

1.386 InitiatedAt(move(p1,p2), t) :- Happens(walking(p1), t) ^ Happens(walking(p2), t) ^ OrientationMove(p1,p2,t) ^ Close(p1,p2,34,t)

// When people stop moving together

// --- walk away
2 TerminatedAt(move(p1,p2), t) :- Happens(walking(p1), t) ^ !Close(p1,p2,34,t)
2 TerminatedAt(move(p1,p2), t) :- Happens(walking(p2), t) ^ !Close(p2,p1,34,t)

// --- both are active
2 TerminatedAt(move(p1,p2), t) :- Happens(active(p1), t) ^ Happens(active(p2),t)

// --- one is active and the other is inactive
2 TerminatedAt(move(p1,p2), t) :- Happens(active(p1), t)  ^ Happens(inactive(p2),t)
2 TerminatedAt(move(p1,p2), t) :- Happens(active(p2), t)  ^ Happens(inactive(p1),t)

// --- start running
2 TerminatedAt(move(p1,p2), t) :- Happens(running(p1), t)
2 TerminatedAt(move(p1,p2), t) :- Happens(running(p2), t)

// --- exit (hard-constrained)
TerminatedAt(move(p1,p2), t) :- Happens(exit(p1),t).
TerminatedAt(move(p1,p2), t) :- Happens(exit(p2),t).

People are meeting

According to the definitions below, meeting is initiated when the people involved interact with each other, i.e., at least one of them is active or inactive, the other is not running, and the measured distance between them is at most 25 pixels. The meeting composite event is terminated when people walk away from each other, or when one of them is running, or has exited the scene.

// When people begin to meet

1.386 InitiatedAt(meet(p1,p2), t) :- Happens(active(p1), t) ^ !Happens(running(p2), t) ^  Close(p1,p2,25,t)

-3.178 InitiatedAt(meet(p1,p2),t) :- Happens(inactive(p1),t) ^ !Happens(running(p2),t)  ^ !Happens(active(p2),t) ^ Close(p1,p2,25,t)

// When people stop meeting

// --- walking
2 TerminatedAt(meet(p1,p2),t) :- Happens(walking(p1),t)  ^ !Close(p1,p2,34,t)
2 TerminatedAt(meet(p1,p2),t) :- Happens(walking(p2),t)  ^ !Close(p2,p1,34,t)

// --- start running
2 TerminatedAt(meet(p1,p2),t)  :- Happens(running(p1),t)
2 TerminatedAt(meet(p1,p2),t) :- Happens(running(p2),t)

// --- exit (hard-constrained)
TerminatedAt(meet(p1,p2),t) :- Happens(exit(p1), t).
TerminatedAt(meet(p1,p2),t) :- Happens(exit(p2), t).


The evidence is composed of ground facts of the input predicates StartTime/1, Happens/2, Close/4, OrientationMove/3, as well as ground function mappings. For example, consider the following fragment:

// Input events:
Enter_ID0 = enter(ID0)
Enter_ID1 = enter(ID1)
Exit_ID0 = exit(ID0)
Exit_ID1 = exit(ID1)
Walking_ID0 = walking(ID0)
Walking_ID1 = walking(ID1)
Running_ID0 = running(ID0)
Running_ID1 = running(ID1)
Active_ID0 = active(ID0)
Active_ID1 = active(ID1)
Inactive_ID0 = inactive(ID0)
Inactive_ID1 = inactive(ID1)

// Output composite events (fluents):
Move_ID0_ID0 = move(ID0, ID0)
Move_ID0_ID1 = move(ID0, ID1)
Move_ID1_ID0 = move(ID1, ID0)
Move_ID1_ID1 = move(ID1, ID1)

Meet_ID0_ID0 = meet(ID0, ID0)
Meet_ID0_ID1 = meet(ID0, ID1)
Meet_ID1_ID0 = meet(ID1, ID0)
Meet_ID1_ID1 = meet(ID1, ID1)

// Facts
// ... sequence of facts ...
Happens(Walking_ID0, 100)
Happens(Walking_ID1, 100)
OrientationMove(ID0, ID1, 100)
Close(ID0, ID1, 34, 100)
// ... sequence of facts ...
Happens(Active_ID0, 170)
Happens(Active_ID1, 170)
// ... sequence of facts ...


Recall that sources from the examples are located in the LoMRF-data project (follow the instructions in Download Example Data), the files of this example are the following: 1. Knowledge base files: * Main MLN file: Data/Examples/Inference/Activity_Recognition/theory.mln * Definitions of moving activity: Data/Examples/Inference/Activity_Recognition/definitions/moving.mln * Definitions of meeting activity: Data/Examples/Inference/Activity_Recognition/definitions/meeting.mln 2. Evidence file: Data/Examples/Inference/Activity_Recognition/narrative.db

Parameters: * Query predicates: HoldsAt/2 * Evidence predicates (Closed-world assumption): StartTime/1, Happens/2, Close/4 and OrientationMove/3.

Marginal inference

lomrf infer -inferType marginal -i theory.mln -e narrative.db -r marginal-out.result -q HoldsAt/2 -cwa StartTime/1,Happens/2,Close/4,OrientationMove/3

MAP inference

lomrf infer -inferType map -i theory.mln -e narrative.db -r map-out.result -q HoldsAt/2 -cwa StartTime/1,Happens/2,Close/4,OrientationMove/3