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AAAI Spring Symposium 2003 on Agent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Modeling context-aware distributed knowledge Jorge Louçã [email protected] ISCTE – Instituto Superior de Ciêncas do Trabalho e da Empresa Lisboa, Portugal 1 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Main Goal and Proposition Research goal : to model multi-dimensional decision-making processes using artificial agents Propositions : • use causal cognitive maps to model software agents • compose a collective solution to a goal through a distributed and incremental process, based on interactions • use context in cognitive maps to define agent’s mental states 2 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Philosophy and artificial intelligence Theoretical foundations: AI as a laboratory Philosophy and Artificial Intelligence: both try to understand all different kinds of perception, action and intelligence The association of this domains allows : • to simulate reasoning in software programs, starting from a given conception of what can be the mind; • to do controlled experiments aiming to understand the knowledge representation systems used in our mind to represent the world; AI as a laboratory : • a specified idea of what is the mind leads to experiments about new software architectures • experiments can be seen as a way of really doing philosophy, because they search the conditions that make possible cognition in general - human intelligence 3 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Philosophy and artificial intelligence Theoretical foundations: AI as a laboratory Intelligence: the capacity of problem solving and decision There is no problem solving and decision capacity without some representation of the world Folk Psychological Constructs help us to model the knowledge representation systems used by our mind, using mental states Advantages of Folk Psychology : operational, comprehensible, an instrument to predict and explain behavior, to manipulate mental states 4 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Philosophy and artificial intelligence Theoretical foundations: Functionalism This use of mental states can act as a functionalist theory if we identify mental states in terms of their causal-functional relationships Functionalism identifies mental content with causal-functional roles [Goldman,93] Causal Covariance Theory of Content [Allen,02] proposes that mental states get their content by being causally related only to what they are about (e.g., to those mental states belonging to its own specific context) This idea of using context to define mental states is adopted to manipulate mental states in a Multi-Agent System, through the use of Cognitive Maps 5 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Cognitive Mapping Theoretical foundations: using cognitive maps to model individual and collective beliefs Cognitive Map : graphical representation of individual or collective beliefs, regarding a specific domain Cognitive mapping is used by psychologists and decision makers to : understand the behavior of actors participating in a decision making process detect conflicts (incoherent viewpoints) discuss points of view ++ State of the wold Task Goal very positive Influence fortementinfluence positive + - Influence positive positive influence Influence negative -- negative influence Influence fortement negative [Carlson et Walden,96] very negative influence 6 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Cognitive Mapping Theoretical foundations: using cognitive maps to model individual and collective beliefs t6 - Create propositions + e1 - Age of employees : 30 / 35 + (-,++) e2 - Employees notion of "group" t1 - Interact (- -,++) (+,++) t2 - Internal debates b2 - Inovate working processes ++ ++ t8 - Invest in R & D + t9 - Professionnal learning + ++ t7 - Research to improve working processes e3 - Employees confidence b1 - Adapt employees to changes (-,+) -- t3 - Accept suggestions + t4 - Periodical reunions + + t5 - Dialogue e4 - Resistance to change working habitudes Example of a partial cognitive map [Louçã,00] Reflexive character of CM - we each construct our private versions of reality and deal only with those constructions, which may or may not correspond to some real world [Lissack & Ross,99] 7 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Cognitive Mapping Theoretical foundations: using cognitive maps to model individual and collective beliefs Collective interpretation using CM - according to Karl Weick and others, organizations can also be seen, at another abstraction level, as systems of construction and interpretation of reality [Weick,95] [Lissack & Gunz,99] e1 - Age du e2 - Cohésion personnel : 30 / 35 + t1 - Interagir construction t2 - Faire réflexion interne and + t9 - Faire formati professionnelle + t3 - Accepter suggestions b1 - Adapter le personnel au changement + --- + + t4 - Faire des réunions périodiques du personnel ++ ++ Different levels of interpretation of reality: individual collective e3 - Confiance du personnel - t5 - Dialoguer e4 - Résistances à changer d'habitudes de travail CM facilitate reasoning, communication and discussion about individual and corporate knowledge 8 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Research in AI using Cognitive Maps Theoretical foundations: state of the art [ M a r c h a n t, 99] [ W e llm a n , 94a] [ C o n s o le e t a l.,8 9 ] [Z h an g et a l., 92 et 94] [P a rk & K im , 95] IA d o m a in In f e re n c e fro m fu z z y im p lic a tio n In fe r e n c e in cau sal n e tw o r k s C o g n itiv e m ap as a lo g ic a l s y s te m C ausal in f e r e n c e ; MAS ; n e u ra l n e tw o rk s N P N L o g ic C ausal in f e r e n c e w ith c ir c u its F o rm a l m odel C au sal in f e r e n c e in q u a lita tiv e p r o b a b ilis tic n e tw o r k s C o g n itiv e M ap K in d o f lin k s b e tw e e n c o n c e p ts R e a s o n in g fro m C o g n itiv e M aps D e te c t a n d s o lv e c o n flic ts in MAS Fuzzy im p lic a tio n P r o b a b ilis tic c a u s a lity C a u s a lity yes yes no no C a u s a l lin k as an im p lic a tio n f o r m u la [ C h a ib d ra a , 97a, 97b et 98] C ausal in fe r e n c e ; MAS C o g n itiv e M ap C a u s a lity L o g iq u e e t R e la tio n s N P N L o g ic and R e la tio n s w ith c ir c u its C a u s a lity R eason f ro m c a u s a l n e tw o r k s yes yes yes no yes no yes C a u s a lity 9 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Modeling context-aware distributed knowledge Proposition: distributed and incremental reasoning process An agent uses the set of concepts represented in its cognitive map. The agent composes its individual solution to the goal, represented by a partial cognitive map. Each agent that receives an allocation message including a sub-goal, starts its own reasoning process and, in return, responds with a solution to the sub-goal. This distributed reasoning process allows representing several points of view concerning the subgoals. Individual solution to a goal Goal allocation : results from other reasoning processes Solution to an allocated goal Solution to an allocated goal Solution to an allocated goal ----------------Cognitive Map Aggregation------------------------- Collective Solution Mat rix c d en Ev i com posi Interaction Matrix tion cts nfli e co Collective Solution with conflicts 10 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Modeling context-aware distributed knowledge Proposition: an incremental, distributed and incremental reasoning process The composition of the interaction matrix is done through the NPNe Methodology of Aggregating Cognitive Maps [Louçã,02a]. Only the most acute opinions are considered to compose the collective solution. A link such as (+,--) evidences a conflict in the organization. This way, the collective reasoning mechanism will detect and evidence conflicts in the collective solution, graphically represented in the form of a cognitive map, allowing a clear discussion and negotiation between the actors. Individual solution to a goal Goal allocation : results from other reasoning processes Solution to an allocated goal Solution to an allocated goal Solution to an allocated goal ----------------Cognitive Map Aggregation------------------------- Collective Solution Mat rix ce c d en Ev i com posi Interaction Matrix tion s lict onf Collective Solution with conflicts 11 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Modeling context-aware distributed knowledge Propositions: cognitive maps standing for mental states Cognitive maps composed on one hand by concepts and by causal links between those concepts, in a strictu senso way [Weik,79] and on the other hand to consider the context of concepts, allowing some kind of inference This way, we can define mental states from cognitive maps by getting their content from the concepts being causally related to their context. More precisely, a mental state is represented by a concept and its context. In cognitive mapping terms we have : (1) concepts can’t be understood without context, (2) the context of a concept is composed by concepts that influence and that are influenced by the main concept and by links between those concepts, (3) each concept is coupled to its context, which can be called a scheme [Bougon & Komocar,94] 12 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Modeling context-aware distributed knowledge Propositions: cognitive maps standing for mental states t6 - Create propositions + e1 - Age of employees : 30 / 35 + (-,++) e2 - Employees notion of "group" t1 - Interact (- -,++) (+,++) t2 - Internal debates b2 - Inovate working processes ++ ++ t8 - Invest in R & D + t9 - Professionnal learning + ++ t7 - Research to improve working processes e3 - Employees confidence b1 - Adapt employees to changes (-,+) -- t3 - Accept suggestions + t4 - Periodical reunions + + t5 - Dialogue e4 - Resistance to change working habitudes Scheme – a concept and its context [Louçã,02] 13 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Modeling context-aware distributed knowledge Propositions: cognitive maps standing for mental states Structure Cognitive Modèle Social Modèle du Domaine Module d'Analyse du Domaine Structure de Raisonnement et de Communication Module de Résolution to interact agents communicate schemes communicating schemes influence agent’s mental states and develops collective knowledge Module d'Analyse Sociale Scheme Module de Communication e1 - Age du e2 personnel : 30 / 35 Transfert de données e3 - Cohésion du personnel - - Confiance du personnel Flux de Contrôle + t1 - Interagir + ++ t2 - Faire réflexion interne t9 - Faire formati professionnelle + b1 - Adapter le ++ personnel au changement Structure Cognitive --- - Accepter suggestions - Faire des réunions périodiques + + t4 + Modèle Social Modèle du Domaine t3 t5 e4 - Dialoguer - Résistances à changer d'habitudes de travail Module d'Analyse du Domaine Structure de Raisonnement et de Communication Module de Résolution Transfert de données If concepts are similar and if there are conflicts, then the graphical interface supports discussion (and argumentation) between individuals, to converge to a common scheme Module d'Analyse Sociale Module de Communication Flux de Contrôle Différents niveaux de détail dans la visualisation d’une solution collective avec mise en évidence de conflits If concepts are not similar, then “CM aggregation” 14 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Application Prototype and application in an industrial entreprise These propositions above were tested in StrAgent, a distributed software system to support decision-making in human organizations SETCOM – Electronica, S.A. (Palmela) industrial enterprise in the domain of telecommunications and electronics Cognitive Maps were built from documents and interviews 5 teams - organisation, products, markets, human resources and information departments Main goal : to model collective discussion and consensual decision • to represent actors knowledge • to show conflicts • to support conflict resolution 15 AAAI Spring Symposium 2003 on AgentAgent-Mediated Knowledge Management (AMKM 2003) San Francisco, Francisco, 24 24-26 March 2003 2003 Conclusion A model of multi-dimensional reasoning in a multi-agent system is proposed. Cognitive maps are used as instruments to represent agent’s mental states. Schemes are used to compose a collective solution to a goal through a distributed and incremental process, based on agent’s interactions. The emergence of collective knowledge is represented in the cognitive map of each agent. 16