Protos is a factored IPOMDP solver developed at THINC Lab @ UGA. It uses Jesse Hoey's implementation of the symbolic Perseus and with some modifications, extends it to I-POMDPs.
❗ The solver has only yet been tested on Linux systems. Running it on Windows might cause problems because the paths are hardcoded in Linux format.
❗ The solver works on Java 15 and above. Please use a Coretto VM or a GraalVM build of JVM. This might seriously help with performance. I haven't performed any formal benchmarks though.
❗ Protos is constantly being developed and likely to undergo major changes as new features are added. If you are using this solver for research work, I would highly recommend cloning or forking the repo and having a local copy.
If you use this solver in your research, please cite the following paper.
Shinde, Aditya, Prashant Doshi, and Omid Setayeshfar. "Cyber Attack Intent
Recognition and Active Deception using Factored Interactive POMDPs." Proceedings
of the 20th International Conference on Autonomous Agents and MultiAgent
Systems. 2021.
@inproceedings{shinde2021cyber,
title={Cyber Attack Intent Recognition and Active Deception using Factored
Interactive POMDPs},
author={Shinde, Aditya and Doshi, Prashant and Setayeshfar, Omid},
booktitle={Proceedings of the 20th International Conference on Autonomous
Agents and MultiAgent Systems},
pages={1200--1208},
year={2021}
}
Run the gradle wrapper
$ ./gradlew shadowJar
The solver should build to a JAR file build/libs/Protos-all.jar
java -jar Protos-all.jar -d <domain_file>
The I-POMDP domain file follows an extension of the SPUDD format for representing ADDs in plaintext. Currently, the solver can only solve 2-agent interactions.
Here is the domain file for a level 2 multi-agent tiger problem. The file
can be found in the domains directory and run directly to get a policy tree
for the L2 tiger I-POMDP. The policy tree will be written to a file in dot
format for graphviz
-- Lines beginning with '--' are single line comments
-- Any domain file should first define all the random variables that are going
-- to be used for the problem.
-- In this case, for the L2 multi-agent tiger problem, we will begin by defining
-- the physical state variable for the tiger location states.
(defvar TigerLoc (TL TR))
-- In the multi-agent tiger problem, there are two agents. Let's call them agent
-- I and agent J. Let's assume that J is at level 0 and level 2 and I at level 1
-- Let's define a model variable for agent I at L1 modeling J at L0. Let's begin
-- with 3 initial models. We will define these models later.
(defvar agent_j (m0 m1 m2))
-- Now, let's define the model variable for J at L2 modeling I at L1.
(defvar agent_il1 (m0 m1))
-- I-POMDPs allow agents to model opponents with different frames. These frames
-- can also be represented as random variables. In this example, we will assume
-- only a single frame. However, the solver does support multiple frames at all
-- levels.
(defvar Frame_jl1 (frame1))
(defvar Frame_il2 (frame1))
-- Add a random variable for agent I's actions
(defvar Agent_actions (OL OR L))
-- Another one for agent J's actions
(defvar Agentj_actions (OL OR L))
-- Random variable for J's observations
(defvar Growl_j (GL GR))
(defvar Creak_j (CL CR SL))
-- Agent I's observations
(defvar Growl (GL GR))
(defvar Creak (CL CR SL))
-- Initialize agent I's L1 model variable representing J's L0 models
-- The three models that we defined on top can now be populated with actual
-- beliefs. In this case, we start we 3 point beliefs. One uniform distribution,
-- and 2 beliefs with 85% probability of the tiger being on either side.
-- The format for defining models is,
-- (<frame_name> (<model_name> (<DD>)))
-- more details and tutorial will be provided in an accompanying tutorial. But
-- for now, here's the initialization of the L1 model variable.
(initmodelvar agent_j
(frames Frame_jl1)
(
(frame1 (m0 (0.5)))
(frame1 (m1 (
(TigerLoc
(TL (0.85))
(TR (0.15))
)
)))
(frame1 (m2 (
(TigerLoc
(TL (0.15))
(TR (0.85))
)
)))
)
)
-- DD's for transitions, observations, beliefs and rewards
-- Common initial belief for all agents
(defdd initLoc
(TigerLoc UNIFORM)
)
-- Transition if anyone opens door
(defdd openDoorDD
(TigerLoc' UNIFORM)
)
-- J's L0 assumption over I's action distribution.
-- Since agent J at L0 cannot directly model agent I at L1, it will assume a
-- static distribution over I's possible actions.
(defdd aIDist
(Agent_actions
(OL (0.005))
(OR (0.005))
(L (0.99))
)
)
-- J's observation function for listening to growls
(defdd growlObsDD
(TigerLoc'
(TL
(Growl_j'
(GL (0.85))
(GR (0.15))
)
)
(TR
(Growl_j'
(GL (0.15))
(GR (0.85))
)
)
)
)
-- I's observation function for listening to creaks
(defdd iListensCreak
(Agentj_actions
(OL
(Creak'
(CL (0.9))
(CR (0.05))
(SL (0.05))
)
)
(OR
(Creak'
(CL (0.05))
(CR (0.9))
(SL (0.05))
)
)
(L
(Creak'
(CL (0.05))
(CR (0.05))
(SL (0.9))
)
)
)
)
-- I's observation function for listening to growls
(defdd iListensGrowl
(TigerLoc'
(TL (Growl' (GL (0.85)) (GR (0.15))))
(TR (Growl' (GR (0.85)) (GL (0.15))))
)
)
-- I's joint action transition for the listen action
(defdd iListensDD
(Agentj_actions
(OL
(TigerLoc' UNIFORM)
)
(OR
(TigerLoc' UNIFORM)
)
(L
(TigerLoc
(TL (TigerLoc' TL))
(TR (TigerLoc' TR))
)
)
)
)
-- J's joint action transition for the listen action
(defdd jListensDD
(Agent_actions
(OL (TigerLoc' UNIFORM))
(OR (TigerLoc' UNIFORM))
(L (SAME TigerLoc))
)
)
-- For the transition and the observation functions, we use DBNs (Dynamic
-- Bayesian Networks). A detailed tutorial will explain how to define
-- these custom DBNs, but for now, here's the DBN for the open action
-- taken by agent I
-- I's DBN for open action
(defdbn actionIOpen
(TigerLoc (TigerLoc' UNIFORM))
(Growl (Growl' UNIFORM))
(Creak (Creak' UNIFORM))
)
-- I's DBN for listen action
(defdbn actionIListen
(TigerLoc (iListensDD))
(Growl (iListensGrowl))
(Creak (iListensCreak))
)
-- J's DBNs
-- J's L0 DBN for opening doors
(defdbn actionOpenAny
(TigerLoc (openDoorDD))
(Growl_j (Growl_j' UNIFORM))
)
-- J's L0 DBN for listening
-- Notice that the Agent_actions var is summed out after multiplying with
-- assumed distribution of I's actions. This is mainly because at L0, agent J
-- does not explicitly model agent I
(defdbn actionL
(TigerLoc (# (Agent_actions) (aIDist * jListensDD)))
(Growl_j (growlObsDD))
)
-- J's level N DBN for opening doors
(defdbn actionJOpen
(TigerLoc (TigerLoc' UNIFORM))
(Growl_j (Growl_j' UNIFORM))
(Creak_j (Creak_j' UNIFORM))
)
-- J's level N DBN for listening
(defdd jListensCreakDD
(Agent_actions
(OL
(Creak_j'
(CL (0.9))
(CR (0.05))
(SL (0.05))
)
)
(OR
(Creak_j'
(CL (0.05))
(CR (0.9))
(SL (0.05))
)
)
(L
(Creak_j'
(CL (0.05))
(CR (0.05))
(SL (0.9))
)
)
)
)
(defdbn actionJListens
(TigerLoc (jListensDD))
(Growl_j (growlObsDD))
(Creak_j (jListensCreakDD))
)
-- Define agents
-- Agnet J L0
(defpomdp agentJ
(S
(TigerLoc)
)
(O
(Growl_j)
)
(A Agentj_actions)
(dynamics
(OL (actionOpenAny))
(OR (actionOpenAny))
(L (actionL))
)
(R
(OL (TigerLoc
(TL (-100))
(TR (10))
))
(OR (TigerLoc
(TL (10))
(TR (-100))
))
(L (-1))
)
(discount 0.9)
)
-- Agent I L1
(defipomdp agentI
(S
(TigerLoc)
)
(O
(Growl Creak)
)
(A Agent_actions)
(Aj Agentj_actions)
(Mj agent_j)
(Thetaj Frame_jl1
(frame1 agentJ)
)
(dynamics
(OL (actionIOpen))
(OR (actionIOpen))
(L (actionIListen))
)
(R
(OL
(TigerLoc
(TL (-100))
(TR (10))
)
)
(OR
(TigerLoc
(TR (-100))
(TL (10))
)
)
(L (-1))
)
(discount 0.9)
(H 4)
)
-- Initialize models for L2 model var
(initmodelvar agent_il1
(frames Frame_il2)
(
(frame1 (m0 (agent_j m0)))
(frame1 (m1 (agent_j m1)))
)
)
-- Agent J L2
(defipomdp agentJl2
(S
(TigerLoc)
)
(O
(Growl_j Creak_j)
)
(A Agentj_actions)
(Aj Agent_actions)
(Mj agent_il1)
(Thetaj Frame_il2
(frame1 agentI)
)
(dynamics
(OL (actionJOpen))
(OR (actionJOpen))
(L (actionJListens))
)
(R
(OL
(TigerLoc
(TL (-100))
(TR (10))
)
)
(OR
(TigerLoc
(TR (-100))
(TL (10))
)
)
(L (-1))
)
(discount 0.9)
(H 4)
)
-- Initial belief of the L2 agent
(defdd initL2agentJ
(
(agent_il1
(m0 (0.5))
(m1 (0.5))
)
* (0.5)
)
)
-- Solvers
(defpbvisolv ipomdp agentJl2Solver)
-- run the solver
-- To run the solver, the syntax is:
-- (defpol <policy_name> = solve <solver_name> (<list of initial beliefs>)
-- <agent_name> <backups> <depth of belief search>)
(run
(defpol l2pol = solve agentJl2Solver ((initL2agentJ)) agentJl2 100 3)
(poltree (initL2agentJ) agentJl2 l2pol 3)
)
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