BRECCIA: A Multi-Agent Data Fusion and Decision Support Framework for Dynamic Mission Planning

Transcription

1 BRECCIA: A Multi-Agent Data Fusion and Decision Support Framework for Dynamic Mission Planning David Sacharny, Tom Henderson, Robert Simmons, Amar Mitiche, Xiuyi Fan and Taylor Welker Cambridge, MA 7 August

2 Colleagues David Sacharny Tom Henderson Robert Simmons Taylor Welker Xiuyi Fan Nanyang Technological University Amar Mitiche INRS Montreal 2

3 Acknowledgment This material is based upon work supported by the Air Force Office of Scientific Research under award number FA (DDDAS-based Geospatial Intelligence) 3

4 BRECCIA 4

5 BRECCIA 5

6 BRECCIA and DDDAS 1. Applications Modeling: e.g., Wind/Obscurant Simulations 6

7 BRECCIA and DDDAS 2. Advances in Mathematical and Statistical Algorithms: e.g., Probabilistic Logic No Weak Methods 7

8 BRECCIA and DDDAS 3. Application Measurement Systems and Methods: e.g., Path Planning 8

9 BRECCIA and DDDAS 4. Software Infrastructures and System: e.g., BRECCIA Multi-agent Server 9

10 BRECCIA: Summary Provides middleware for: real-time coupling of computation and knowledge across heterogeneous platforms 10

11 BRECCIA: Summary Provides uncertainty analysis for mission planning involving combination of: human statements simulation results sensor measurements 11

12 BRECCIA: Summary Agents driven by uncertainty reduction: identification of major uncertainty sources uncertainty quantification propose measures for uncertainty reduction 12

13 Two Main Results Probabilistic Logic New approach: system of nonlinear equations Results on large systems Multi-agent Middleware Real-time agent mission assessment and replanning 13

14 Given: Uncertainty in Knowledge A set of propositions Bases (e.g., S 1 : UAV 1 is operational) A set of probabilities for the propositions (e.g., (S 1 )[0.9]) Then, given a query (e.g., Q: Mission_OK), determine its probability 14

15 Probabilistic Logic SAT (Satisfiability Problem) Given a propositional calculus formula, find a truth assignment to each logical variable so that the formula is true E.g., S P & (P Q) (i.e., Modus Ponens premises) P True and Q True satisfies S 15

16 Probabilistic Logic PSAT (Probabilistic SAT Problem) (Simple version!) Given a CNF formula, and a probability assignment for each conjunct, find a consistent probability assignment for a query formula E.g.: [0.7] S 1 : P [0.7] S 2 : P v Q [?] Query: Q 16

17 To Solve: Count Models % Counts Models Sentences Probabilities t 1 t 2 t 3 t 4 P Q P P v Q ω Ω models P t 3 +t 4 =

18 To Solve: Count Models Counts Models Sentences Probabilities t 1 t 2 t 3 t 4 P Q P P v Q ω Ω models P v Q t 1 + t 2 + t 4 =

19 To Solve: Count Models Solve: t 3 + t 4 = 0.7 t 1 + t 2 + t 4 = 0.7 t 1 + t 2 + t 3 + t 4 = 1 add constraint So solve: t 1 t 2 t 3 t 4 = E.g.: t 1 t 2 t 3 t 4 =

20 To Solve: Count Models Solve: t 3 + t 4 = 0.7 t 1 + t 2 + t 4 = 0.7 t 1 + t 2 + t 3 + t 4 = 1 add constraint So solve: t 1 t 2 t 3 t 4 = E.g.: t 1 t 2 t 3 t 4 =

21 To Solve: Count Models So solve query, e.g., P(Q), sum probabilities of models of formula: E.g., for t 1 t 2 t 3 t 4 = then P(Q) = t 2 + t 4 = 0.4 E.g., for t 1 t 2 t 3 t 4 = then P(Q) = t 2 + t 4 =

22 Thimm s Formulation A constraint r, is a disjunction with a probability. Ω is the set of all complete conjunctions (a literal from every logical variable appears once and only once in a complete conjunction). 22

23 Geometric View of Query 23

24 Example of Ω 24

25 Linear Formulation Issues Exponential Complexity (in number of sentences) Uses SAT solvers to produce the matrix Solver Complexity: Constraints: 0 p i 1 Multiple solutions 25

26 New Formulation Assume Boolean random variables are independent Express each disjunction clause as: P(AvB) = P(A) + P(B) P(A^B) = P(A) + P(B) P(A)P(B) Develop system of nonlinear equations and solve for atom probabilities; use these to solve query 26

27 Example: Modus Ponens 27

28 Some Observations Given n-modus Ponens: A 1 A 1 A 2 A n 1 A n then standard approach needs 2 n models, we solve it in linear time 28

29 Some Observations Consider Russell & Norvig Wumpus World: With logical variables: breeze: e.g., B11 Gold: e.g., G23 Pit: e.g., P22 Stench: e.g., S44 Wumpus: e.g., W34 Rules like: P21 v B11 16*5 = 80 variables Given the rules, there are: 583 sentences 29

30 Some Observations (cont d) Breezes Gold Pits Stench Wumpus 30

31 Some Observations (cont d) 31

32 URBAN: Uncertainty Reduction- Based Agent Network A mult-agent system specifically designed for geospatial-temporal analysis across massive distributed datasets. Leverages the GeoWave project developed at the National Geospatial- Intelligence Agency (NGA) ( and the open source frameworks Apache Hadoop (for distributed processing) and Accumulo (for key/value database storage). Conceptually, a layer atop GeoWave that provides probabilistic logical reasoning over space and time. Dissemination of knowledge in the form of probabilistic sentences and maps published to GeoServer ( Addresses tasking, processing, exploitation, and dissemination of data (TPED) with an agile sensor network and the unifying concept of uncertainty reduction. 32

33 URBAN Implementation The BRECCIA Agent represents the core abstraction for all agents in the system. Agents are distributed across specialized machines such as UAVs, mobile laptops, or high performance computers. BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions :Mission Planner Hadoop Accumulo GeoServer DB RRT* Planner The inherited components of each BRECCIA agent enable an overall system that is dynamic and datadriven. :Weather Monitor :UAV Manager :User FExample Instantiations of the BRECCIA Agent 33

34 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions The Belief-Desire-Intention (BDI) engine serves a dual purpose As a software architecture it facilitates the discussion and design of agents As a software cognitive model it enables goal-driven behavior (and in our particular implementation data-driven). Jason ( provides the language interpreter and BDI engine to BRECCIA agents. 34 The Jason Reasoning Cycle. From Programming Multi-Agent Systems in AgentSpeak Using Jason (pg. 68), by Rafael H. Bordini et al., 2007, England: John Wiley and Sons Ltd.

35 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions 35

36 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions How does Jason enable data-driven behavior? Plans are executed due to events which may be achievement requests or a change in belief. Example: Consider the case where a UAV is executing a path and periodically querying the geospatial database for path obstruction. To cause the agent to re-plan in the event of an obstruction, the code is as follows: +path_obstructed(pathname) ->!replan(pathname) React to the belief that path is obstructed by replanning The language defined by Jason is inherently data-driven. 36

37 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions How do Uncertainty Reduction and Probabilistic Logic achieve an agile sensor network and TPED? Probabilistic logic and quantified uncertainty provide a means for applying well-known planning algorithms to an abstract problem space. Certainty as a reward for plans: 37 Rewards are higher for truth and certainty of positive sentences, or falsity and certainty for negative sentences. Rewards are lowest for uncertain sentences, i.e. p=0.5. Non-biased sentences (i.e. inference rules) have a symmetrical reward function. The uncertainty reduction goal is executed by a plan to maximize the certainty reward

38 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions Uncertainty Reduction Example Consider the case where an analyst is cooperating with a UAV to gather information about a location. Associated with a path plan for the UAV are the following sentences and inference for mission success: Plan A: a:path_obstructed[p=0.1], b:battery_ok[p=0.9], c:target_recorded[p=0.7], b^c -> mission_success [p=0.84] During the course of the mission, a second analyst, cooperating on an unrelated mission, reports seeing smoke at a location that crosses Plan A s path. Belief a updates, generates an event and causes the UAV agent to replan in this case two plans are generated, one to continue on with the mission and another to turn back. Plan B: a:path_obstructed[p=0.1], b:battery_ok[p=0.8], c:target_recorded[p=0.7], b^c -> mission_success [p=0.82] Plan C: a:path_obstructed[p=0.05], b:battery_ok[p=0.95], c:target_recorded[p=0.2], b^c -> mission_success [p=0.47] Target UAV Plan A Plan B Plan C 38 Legend

39 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions Uncertainty Reduction Example Plan A: a:path_obstructed[p=0.6], b:battery_ok[p=0.9], c:target_recorded[p=0.7], b^c -> mission_success [p=0.84] Reward: R(a,0.6) + R(b,0.9) + R(c,0.7) + R(0.84) = 0.3 Plan B: a:path_obstructed[p=0.1], b:battery_ok[p=0.8], c:target_recorded[p=0.7], b^c -> mission_success [p=0.82] Reward: R(a,0.1) + R(b,0.8) + R(c,0.7) + R(0.82) = 0.45 Plan C: a:path_obstructed[p=0.05], b:battery_ok[p=0.95], c:target_recorded[p=0.2], b^c -> mission_success [p=0.47] Reward: R(a,0.05) + R(b,0.95) + R(c,0.2) + R(0.47) = 0.42 Although the probability of mission success reduced by a small amount due to the uncertainty in battery usage, the overall uncertainty for this set of sentences has decreased by choosing Plan B. Target UAV Plan A Plan B Plan C 39 Legend

40 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions Uncertainty Reduction Example Demonstrates how uncertainty reduction and probabilistic logic facilitate planning over heterogeneous data paths, events, human observations. Provides justification for decision makers the agent may request guidance from the user if the plan alternatives are close to one another 40

41 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions GeoWave Connector GeoWave enables agents to simultaneously access a distributed geospatial-temporal database. Agents publish geospatial knowledge, written to the database, via GeoServer. This enables remote sharing of this type of knowledge. In Jason, internal actions coded into the GeoWave connector provide direct access to the databases. Example from weather agent: +!share_storm_info(location, Agent) -> geowaveconnector::get_wms_url(location, WmsUrl) ;.send(agent, tell, storm_info(wmsurl). Data-driven response from UAV agent: +storm_info(wmsurl) ->!check_path_obstruction(wmsurl) 41

42 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions Distributed approach enables web-based user access: 42 Prototype BRECCIA Client interface and Chat Window Featuring Map of Salt Lake City from Local GeoServer Instance

43 URBAN Implementation BRECCIA Agent BDI Engine Uncertainty Reduct. Goal P.L. Logic Module GeoWave Connector Specialized Functions Specialized Functions Current implementations of specialized functions include Connecting to MATLAB instances (Agents who know how to use MATLAB) RRT* path planner (Agents who know how to plan over space with vehicle constraints) Wind Simulator (Agent that runs a wind vortex simulator) Ongoing work of specialized functions GDELT database query (Agents that can query the massive GDELT global event database ( OpenWeather API Agent (Agents that can query distributed weather information) UAV simulator (Agents that can run real-time UAV simulators) UAV controller (Agents that can control quadcopters in real-time) 43

44 Conclusions Developed effective and efficient probabilistic logic method Developed core of BRECCIA system URBAN: allows communicating, autonomous agents Cloud computing 44

45 Future Work Probabilistic Logic Use conditional probabilities P(A&B) = P(A B)P(B) approximate P(A 1 & A 2 & & A n ) by upper bound: min(p(a i & A j )), i, j = 1: n Extend to First Order Logic Exploit argumentation to reduce analysis cost 45

46 Future Work BRECCIA middleware Extend breadth of agents Extend depth of analysis Develop realistic ISR scenario Collaborate with Air Force ISR wing Measure performance Accuracy of uncertainty measures Time & Space Computational aspects 46

47 Future Work Simulation Use full 3D QUIC-URB modeling system Exploit Gaussian process models of 3D features Terrain Temperature Wind Obscurants 47

48 Future Work Experiments Develop real-world ISR missions Small-scale experiments in lab Full-scale experiments (in SLC) Measures of performance (MOP) & Effectiveness(MOE) Success (MOP) Time (MOP) Information adequacy (MOE) 48

49 UTAH UAV Fleet 49

50 Questions? 50