Rigurous Software Development of Multi-Agent Systems
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- Charity Malone
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1 Rigurous Software Development of Multi-Agent Systems 2 nd Prometidos-CM Winter School Speaker: Álvaro Fernández Díaz Universidad Politécnica de Madrid
2 Structure of the talk Multi-Agent Systems Purpose Belief-Desire-Intention RSD of Multi-Agent Systems Jason ejason Summary
3 Multi-Agent Systems High level abstraction for multi-process systems The computational entities are known as intelligent agents Autonomous
4 Multi-Agent Systems High level abstraction for multi-process systems The computational entities are known as intelligent agents Autonomous Interact (e.g. via communication) HELLO!
5 Multi-Agent Systems High level abstraction for multi-process systems The computational entities are known as intelligent agents Autonomous Interact (e.g. via communication) Rational (motivational stance) HELLO!
6 Multi-Agent Systems Purpose: Adapt to changing environments Model uncertainty (non-precise information) Different approaches: Automatons (finite state machines) Rule systems Blackboard Belief-Desire-Intention
7 BDI Architecture [Rao & Georgeff, 1995] Identifies different program concerns: Beliefs: the facts that the agents consider to be true about the world Desires: the objectives or situations that the agents might like to realise Intentions: how the agents have chosen to act in order to achieve its goals
8 Example: Bit Transfer Protocol A sender must transmit a string of bits to a receiver. It can only send one bit at a time.
9 Example: Bit Transfer Protocol A sender must transmit a string of bits to a receiver. It can only send one bit at a time. Sender Receiver
10 Desires The string of bits is correctly transmitted from the sender to the receiver Sender Receiver
11 Desires The string of bits is correctly transmitted from the sender to the receiver Beliefs (sender) The string of bits is BitString
12 Beliefs (sender) The string of bits is BitString The receiver has obtained bit n Desires The string of bits is correctly transmitted from the sender to the receiver
13 Beliefs (sender) The string of bits is BitString The receiver has obtained bit n Desires The string of bits is correctly transmitted from the sender to the receiver Event: Ack message
14 Desires The string of bits is correctly transmitted from the sender to the receiver Beliefs (sender) The string of bits is BitString The receiver has obtained bit n Beliefs (receiver) The string of bits received so far is Event: Ack message
15 Desires The string of bits is correctly transmitted from the sender to the receiver Beliefs (sender) The string of bits is BitString The receiver has obtained bit n Beliefs (receiver) The string of bits received so far is Event: Bit message Event: Ack message
16 Desires The string of bits is correctly transmitted from the sender to the receiver Beliefs (sender) The string of bits is BitString The receiver has obtained bit n Beliefs (receiver) The string of bits received so far is The sender knows that I ve obtained n bits Event: Bit message Event: Ack message
17 Desires The string of bits is correctly transmitted from the sender to the receiver Beliefs (sender) The string of bits is BitString The receiver has obtained bit n Beliefs (receiver) The string of bits received so far is The sender knows that I ve obtained n bits Intentions (sender) If I believe that the receiver has not obtained any bit, I send the first bit to it. Event: Bit message Event: Ack message
18 Desires The string of bits is correctly transmitted from the sender to the receiver Beliefs (sender) The string of bits is BitString The receiver has obtained bit n Beliefs (receiver) The string of bits received so far is The sender knows that I ve obtained n bits Intentions (sender) If I believe that the receiver has not obtained any bit, I send the first bit to it. If I believe that the receiver has obtained the bit in position n, I send the next bit to it. Event: Bit message Event: Ack message
19 Desires The string of bits is correctly transmitted from the sender to the receiver Beliefs (sender) The string of bits is BitString The receiver has obtained bit n Intentions (sender) If I believe that the receiver has not obtained any bit, I send the first bit to it. If I believe that the receiver has obtained the bit in position n, I send the next bit to it. Beliefs (receiver) The string of bits received so far is The sender knows that I ve obtained n bits Intentions (receiver) If I believe that I know the bit in position n, I send an acknowledgement message to the receiver. Event: Bit message Event: Ack message
20 Rigorous Software Development Use of mathematically grounded methods (and logic) in the software development procedure Formal Specification Describe the exact semantics of programs Formal Verification (discover/test properties) Theorem proving Model Checking
21 BDI Logic [Rao & Georgeff, 1998] Introduces the following operators: BEL(φ): the agent believes φ DES(φ): the agent desires φ INTEND(φ): the agent intends φ
22 BDI Logic If the sender agent knows the value of the string of bits and desires to transfer this value to the receiver, then the sender will eventually intend to transfer this value ( BEL snd (value) DES snd (transfer) ) INTEND snd (transfer)
23 BDI Logic If the sender agent knows the value of the string of bits and intends to transfer this value to the receiver, then the receiver will eventually know this value ( BEL snd (value) INTEND snd (transfer) ) BEL rcv (value)
24 RSD in Multi-Agent Systems Different modeling languages available MABLE AGENTSPEAK(F) Formal semantics for agent-oriented programming languages AGENTSPEAK(L) 3APL Jason
25 Jason Programming Language [Bordini & Hübner & Wooldridge, 2007] The agent s beliefs are prolog-like predicates and rules door1(open) even(x) :- X mod 2 == 0. The agent s desires are referred to as goals and represented by events to handle!at(home) represents the desire to be at home?at(home) represents the desire to test whether the agent is at home
26 Jason Programming Language The agent s intentions are represented by stacks of predefined plans that the agents commits itself to carry out!go(place): Place = office & today(workday)!go(car);!drive(office) play_the_boss.
27 Jason Programming Language The agent s intentions are represented by stacks of predefined plans that the agents commits itself to carry out!go(place): Place = office & today(workday)!go(car);!drive(office) play_the_boss.!go(place): Place = office & today(holiday)!go(doctor).
28 Jason Programming Language Reasoning cycle
29 Jason Programming Language (State) Agent Configuration = (ag, name, C, M, T, Phase) Program (plans, knowledge base) intentions (desires, intentions, messages )
30 Jason Programming Language Reasoning cycle
31 Jason Programming Language
32 Jason Programming Language
33 Jason Programming Language Pupil 2 Teacher
34 Jason Programming Language init_count(0). max_count(10). Beliefs!startcount. Goals (Desires) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). Plans (Part of Intentions) +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
35 Jason Programming Language Beliefs init_count(0). max_count(10). Goals (Desires)!startcount. Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
36 Jason Programming Language Beliefs init_count(0). max_count(10). Goals (Desires)!startcount. Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
37 Jason Programming Language Beliefs init_count(0). max_count(10). Goals (Desires)!startcount. Event: +!startcount. Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
38 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
39 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
40 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
41 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
42 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
43 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
44 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
45 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
46 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
47 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(0) <- +actual_count(0). Event: +!startcount. +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
48 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(0) <- +actual_count(0). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
49 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(0) <- +actual_count(0). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
50 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Plans (Part of Intentions) +!startcount : init_count(0) <- +actual_count(0). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
51 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(0) <- +actual_count(0). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
52 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
53 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
54 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
55 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
56 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(0) : max_count(y) & 0 < Y <- -actual_count(0);.send(teacher,tell,actual_count(0). +actual_count(0) : max_count(y) & 0 >= Y <-.print( Terminated Count ).
57 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(0) : max_count(y) & 0 < Y <- -actual_count(0);.send(teacher,tell,actual_count(0). +actual_count(0) : max_count(y) & 0 >= Y <-.print( Terminated Count ).
58 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(0) : max_count(10) & 0 < 10 <- -actual_count(0);.send(teacher,tell,actual_count(0). +actual_count(0) : max_count(10) & 0 >= 10 <-.print( Terminated Count ).
59 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(0) : max_count(10) & 0 < 10 <- -actual_count(0);.send(teacher,tell,actual_count(0). +actual_count(0) : max_count(10) & 0 >= 10 <-.print( Terminated Count ).
60 Jason Programming Language Beliefs init_count(0). max_count(10). actual_count(0). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(0) : max_count(10) & 0 < 10 <- -actual_count(0);.send(teacher,tell,actual_count(0). +actual_count(0) : max_count(10) & 0 >= 10 <-.print( Terminated Count ).
61 Jason Programming Language Beliefs init_count(0). max_count(10). Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). Event: +actual_count(0) +actual_count(0) : max_count(10) & 0 < 10 <- -actual_count(0);.send(teacher,tell,actual_count(0). +actual_count(0) : max_count(10) & 0 >= 10 <-.print( Terminated Count ).
62 Jason Programming Language Beliefs init_count(0). max_count(10). Event: +actual_count(0) Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(0) : max_count(10) & 0 < 10 <- -actual_count(0);.send(teacher,tell,actual_count(0). +actual_count(0) : max_count(10) & 0 >= 10 <-.print( Terminated Count ). actual_count(0)
63 Jason Programming Language 0 actual_count(0) Pupil Teacher
64 RSD in Jason The formal semantics allow the definition of all possible transitions between agent states Where are the modalities BEL, DES, INTEND?
65 BDI Logic in Jason [Bordini & Moreira 2004]
66 BDI Logic in Jason [Bordini & Moreira 2004] Beliefs init_count(0). max_count(10). actual_count(0).
67 BDI Logic in Jason [Bordini & Moreira 2004] Beliefs init_count(0). max_count(10). actual_count(0). BEL init_count 0 BEL init_count 1
![BDI Logic in Jason [Bordini & Moreira 2004] Beliefs init_count(0).](/docs-images/81/84044117/images/68-1.jpg "max_count(10). actual_count(0). BEL init_count 0 BEL init_count 1")
68 BDI Logic in Jason [Bordini & Moreira 2004] Beliefs init_count(0). max_count(10). actual_count(0). BEL init_count 0 BEL init_count 1
69 BDI Logic in Jason [Bordini & Moreira 2004]
70 BDI Logic in Jason [Bordini & Moreira 2004] Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
71 BDI Logic in Jason [Bordini & Moreira 2004] It is true for all the achievement goals in the triggering events of the plans that the agent is committed to carry out Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ).
72 BDI Logic in Jason [Bordini & Moreira 2004] It is true for all the achievement goals in the triggering events of the plans that the agent is committed to carry out Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ). INTEND! start_count
73 BDI Logic in Jason [Bordini & Moreira 2004] It is true for all the achievement goals in the triggering events of the plans that the agent is committed to carry out Plans (Part of Intentions) +!startcount : init_count(x) <- +actual_count(x). +actual_count(x) : max_count(y) & X < Y <- -actual_count(x);.send(teacher,tell,actual_count(x). +actual_count(x) : max_count(y) & X >= Y <-.print( Terminated Count ). INTEND! start_count
74 BDI Logic in Jason [Bordini & Moreira 2004] It is true for all the achievement goals in the triggering events of the plans that the agent is committed to carry out Plans committed (Intentions) +!startcount : init_count(0) <- +actual_count(0). INTEND! start_count
75 BDI Logic in Jason [Bordini & Moreira 2004]
76 BDI Logic in Jason [Bordini & Moreira 2004] Plans committed (Intentions) +!startcount : init_count(0) <- +actual_count(0). INTEND init_count
77 BDI Logic in Jason [Bordini & Moreira 2004] Plans committed (Intentions) +!startcount : init_count(0) <- +actual_count(0). INTEND init_count Event: +actual_count(0) Event: +!next_num(0)
78 BDI Logic in Jason [Bordini & Moreira 2004] Plans committed (Intentions) +!startcount : init_count(0) <- +actual_count(0). Event: +actual_count(0) Event: +!next_num(0) INTEND init_count INTEND next_num(0)
79 RSD in Jason The source code of an agent defines its initial state S 0 S 0
80 RSD in Jason The source code of an agent defines its initial state S 0 The different transition rules allow the generation of the state space (i.e. all possible states of the system) S 0 S 1 S 2 S 3 S n
81 RSD in Jason The source code of an agent defines its initial state S 0 The different transition rules allow the generation of the state space (i.e. all possible states of the system) The BDI predicates satisfied by each state can be computed S 0 S 1 S 2 S 3 S n
82 RSD in Jason The source code of an agent defines its initial state S 0 The different transition rules allow the generation of the state space (i.e. all possible states of the system) The BDI predicates satisfied by each state can be computed The properties can then be checked S 0 S 1 S 2 S 3 S n
83 ejason [Fernández & Benac & Fredlund, 2013] Implementation of the Jason interpreter in Erlang Efficient concurrency Distribution Fault Tolerance (robustness) The implementation of the Jason interpreter was facilitated by the existence of a formal semantics
84 ejason [Fernández & Benac & Fredlund, 2013] One of the main contributions claimed is the introduction of distribution transparency Logic of control for agents is decoupled from system orchestration at runtime Relies on the uniqueness of the agent names If agents can spawn other agents, can the uniqueness of names be guaranteed?
85 ejason [Fernández & Benac & Fredlund, 2013] A formal semantics has been developed for the whole extension
86 ejason [Fernández & Benac & Fredlund, 2013] Uniqueness of agent names has been shown using theorem proving techniques Considering a system with two namesake agents Showing by contradiction that such a system cannot be built
87 Takeaways Rigurous software development is also available for Multi-Agent Systems Jason is a mathematically grounded agentoriented programming language Unambiguous program semantics Formal verification can be utilised Implementation of interpreters is facilitated
88 Takeaways At UPM we carry on related research on the ejason programming language Reimplementation of an interpreter of Jason Augmented functionality Formal semantics for the extension is available
89 Takeaways At UPM we carry on related research on the ejason programming language Reimplementation of an interpreter of Jason Augmented functionality Formal semantics for the extension is available Contact:
90 References [Rao & Georgeff, 1995] BDI Agents: From theory to practice, International Conference on Multi-Agent Systems (ICMAS 95) San Francisco, CA. [Rao & Georgeff, 1998] Decision procedures for BDI logics, Journal of Logic and Computation
91 References [Bordini & Moreira, 2004] Proving BDI properties of agent-oriented programming languages, Annals of Mathematics and Artificial Intelligence [Fernández & Benac & Fredlund, 2013] Adding distribution and Fault Tolerance to Jason, to appear in Journal of Science of Computer Programming
92 Questions & Answers
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