RTEC_s^RCT: Run-Time Event Calculus

RTEC_s^RCT is an extension of RTEC, an open-source Event Calculus dialect optimised for data stream reasoning, with sequence support.
It is written in Prolog and has been tested under SWI-Prolog on Linux.

---

License

RTEC_s^RCT comes with ABSOLUTELY NO WARRANTY.
This is free software, and you are welcome to redistribute it under certain conditions; see the
GNU Lesser General Public License v3 for more details.

---

Related Software

• RTEC: Original version of RTEC.
• iRTEC: Incremental RTEC. iRTEC supports incremental reasoning, efficiently handling delays and retractions in data streams.
• oPIEC: Online Probabilistic Interval-Based Event Calculus. Supports reasoning over data streams under uncertainty.
• Wayeb: Complex Event Processing and Forecasting (CEP/F) engine written in Scala, based on symbolic automata and Markov models.

---

Experiments

Dataset setup

1. Download the maritime, voting and fleet managements datasets using the links in files:

   RTECsRCT/examples/{maritime,voting,fleet_management}/dataset/csv/data.txt

2. Place the CSV files in:

   RTECsRCT/examples/{maritime,voting,fleet_management}/dataset/csv/

---

Running experiments

Navigate to:

/RTECsRCT/execution_scripts

Then run the appropriate script:

• MSA-synth

  • run_MSA_synth_s_rct.sh (for RTEC_s^RCT)
  • run_MSA_synth_s.sh (for RTEC_s)

• MSA-Brest

  • run_MSA_brest_s_rct.sh (for RTEC_s^RCT)
  • run_MSA_brest_s.sh (for RTEC_s)

• MAS-Voting

  • run_voting_s_rct.sh (for RTEC_s^RCT)
  • run_voting_s.sh (for RTEC_s)

• FM

  • run_FM_s_rct.sh (for RTEC_s^RCT)
  • run_FM_s.sh (for RTEC_s)

---

Results

Results can be found in:

/RTECsRCT/examples/{maritime,voting,fleet_management}/results/

---

Summarising results

After running the maritime and voting experiments, call the extraction script from the corresponding results folder to get a summary of all the experiments.

Example:

/RTECsRCT/examples/maritime/results/synth/extract_data.sh

---

Computing eagerness

To compute avg eagerness for an experiment:

1. Navigate to:

   /RTECsRCT/execution_scripts/
   

2. Run compute.py on the results and dataset (call with --help for help). Note: For fleet management, to exclude long vehicle gaps run with the --fm flag.

Example (MAS 1000 agents - sequence length 20 timepoints)

python3 compute.py \
  --rec ../../examples/voting/results/1000_20/log-swi-10-10-csv-file-recognised-intervals.txt \
  --dt  ../../examples/voting/dataset/csv/voting-1000-1-20.csv