Sustainability (and other stories) HART to earth Sustainability (and other stories)

Agenda Sustainability: role for HART Modeling for HART Video!!!

HART for sustainability Sustainability issues New solutions New insights New methodologies New challenges New tools New applications HART

Sustainability: more than environment environmental, economic and social issues are interlinked economy only exists in the context of a society, both society and economic activity are constrained by the earth’s natural systems. A secure future depends upon the health of all three environment economy society Sustainable Development encompasses balancing environmental, economic, and societal needs.

Social and economic development Millennium Development Goals (UN) to be achieved by 2015 or 2020 Hunger and poverty, education, women’s position, children and mothers, health, environmental sustainability Global partnership for development

HART themes for sustainability Collaboration Sensor networks Intelligent systems Distance, distributed, teams Simulation ABMS What if analysis Augmented reality Gaming Virtual environments

Chances for HART Human built systems and human impact: design, optimization, modeling and impact Energy and resource management; Transportation; Sustainable communities and cities; urban planning; Eco-system monitoring, modeling and management: Models of ecosystem structure and function; species distribution and migration; invasive species; emerging diseases; Pollution: greenhouse gas emissions, toxic pollutants, and agricultural runoff. Economics and human behavior modeling: Human well-being Health support

Integrated model for HART Aim: a framework where social behaviour can be studied For H, A and R Including a generic architecture able to represent social and individual aspects, adaptable to (nature, capabilities, architectures) the individuals, and at different levels of abstraction

What do I do? Agents and organizations Agents Organizations Autonomy Regulation Agents are motivated by their own objectives, beliefs… May take up role in organization if that serves their purposes Organizations have own purpose Exist independently of the agents populating it

Regulation versus Autonomy Regulated, or directed, behavior Pre-determined behavior, external to agent: Lack of agility Do not consider differences in individual capabilities Strict obedience to rules often does not get work done Autonomous behavior Ability to make decisions about own activity Individual rationality is insufficient to deal with social behavior (helpfulness, greater good, …) (Informal) structures are necessary for coordinating processes and stability

Approach: OperA Balance between organizational regulation and individual autonomy Specify interaction independently from agent’s internal design Interaction structures are not completely fixed in advance Enables open systems and heterogeneous participation OperA: separation between agent and role Minimal requirements, dynamic enactment Explicit agreements concerning individual performance Explicit agreements concerning interaction Enables evolving societies OperA: landmarks H A R

Social System Individual Collective Intentional Behavioural Social Cultural Social System Internal External

Collective structures The MASQ Metamodel (Ferber, Stratulat, Tranier, 2009) Individual Collective Internal External Mind Presence/ Object Decisional part of an agent Entities in the environment Culture Space Shared elements Social norms Collective structures

Presence Mind Space Culture BRIDGE Perceptions Influence External state & Capabilities Presence OperA Internal state Mind Collective structures & Interaction rules Space relation Objects Culture Constitutive & Regulative norms

Mind: implementation Model individual decision making Represent the impact of the social on the individuals and what impacts on the social level (simple) rules (complex) reasoning models, e.g. BDI Emulate human behaviour as a conjunction of Reasoning (decision-making) Emotions Personality Personal values (cultural background, ethical or moral beliefs etc.)

The BRIDGE architecture Beliefs Response Intentions Desires Goals Ego The BRIDGE architecture B E D G I Inference method personal ordering Preference Cultural beliefs Normative beliefs Growth needs deficiency needs sense act generate select plan update interpret filter plan select direct select explicit implicit overrule R direct urges, stress stimuli

Presence: Implementation Virtual: agents Physical: robots Simulated humans Capabilities Sensing Acting Communicating Roles: Expected capabilities, objectives, norms

Spaces: Implementation Teamwork Aims and requirements are determined for teamwork Global behaviour is more than ‘sum’ of individual behaviours Social Spaces OperA Organizational Model: Social structure Organization Structure Normative Structure Physical Spaces: tbd

Creating the Social Structure

Creating the Social Environment

Creating the Interaction Structure

Validation Validation checks: Meta-model constraints (is the model an instance of the meta-model?) OperA specific constraints (is the model a correct OperA model?) Validation intended as design-assistance

Culture: Example of Implementation UAI (Uncertainty Avoidance Index). High: high defensive threshold against change; acceptance of government orders. IDV (Individualism). High IDV: agents will follow own goals and beliefs before those of the society PDI (Power Distance Index). High: individuals are more likely to follow norms coming from authorities. Combinations! Eg. high UAI and high PDI leads to a pyramid model of organization. UAI high: Oa φ ≥a Na ¬φ deontic norms are preferred over social norms IDV high: Ga φ ≥a Oa ¬φ individual goals are preferred over norms PDI high: Bfarmer Bofficial φ → Bfarmer φ farmer accepts beliefs of officials

Dynamics: Behaviour change - prescription Mind Presence deliberation sense Culture Space

Dynamics Behaviour change - emergence Mind Presence deliberation sense Culture Space

Final remarks Work in progress ! Next steps: Formalization and implementation of BRIDGE agents (e.g. using 2APL or GOAL frameworks) Culture model OperAtionalization: From OperA model to running simulation (Repast, Brahms) From OperA model to ‘closed’ system: Roles as skeletons for agents (in ALIVE project, using AgentScape) From OperA model to ‘open’ system: Roles as input for agent participation (deliberation, negotiation, with Birna, Catholijn)

Video: you even get to choose! Fun: http://www.digyourowngrave.com/saturday-night-live-old-glory-robot-insurance/ Highlights: http://www.itworld.com/science/130854/2010-robots-review