ASCENS philosophy: simplifying complexity

ASCENS explores perception, adaptation and self-organization offering high-level methods and practical tools for developing intelligent service-component ensembles that organize themselves and act autonomously. Under the motto simplifying complexity, the technological challenge is in controlling the dynamics of the emergent environments. Often we deal with massively parallel systems where harmonizing the communication and optimizing individual and collective goals is a major challenge. For example, how to build a powerful cloud platform that turns a huge number of simple devices into a super computer, or how to optimally organize mobility with electric vehicles taking into account battery restrictions, multiple itineraries and traffic conditions, or how to organize a rescue operation with self-aware and self-healing robot swarms.

The ASCENS approach to such complexity is to deal with issues at a local level, solving problems at a smaller scale and then harmonizing these solutions with more global ones. More concretely, a communication is organized in an implicit manner, who talks to whom is decided in run-time, depending on the current needs and situation. Thus, ASCENS approach decomposes complex systems into small service components (with clearly defined local goals) and then construct larger groups, called ensembles that fulfill collective goals. A criteria to construct an ensemble is a condition i.e. some “more global circumstance”, e.g. “ connect all robots that can carry 4kg and are in the radius of 100m – the goal is to jointly transport 15kg heavy object”. Having the knowledge as a major criterion for communication leads to self-awareness (where awareness can be defined as the knowledge of own functional and operational requirements and states). Making ASCENS components and ensembles aware of their own capabilities and goals ensures adaptive and autonomous behavior at runtime.

ASCENS approach: both pragmatic and formal

Pragmatic orientation means building autonomous systems that do practical things, like autonomous robot swarms performing rescue operation, autonomous cloud computing platforms transforming numerous small computers into a super computing environment or autonomous e-mobility support that ensures energy-aware transportation services. In reality, autonomous behavior means functioning without human intervention, seamlessly using own rules and criteria. At runtime, the more autonomy the system exhibits the less obvious it appears to outside observers. Thus to be sure about correct functioning of such systems it is necessary to develop formal methods and tools that can ensure not only that an autonomous system really does what it is supposed to do, but also that important conditions of the whole controlled environment are never violated. ASCENS offers a range of formal means that ensure modeling, formal reasoning, validation and verification of complex controlled systems, both in its design and at runtime.

ASCENS at ICT 2013 – science and joy
The picture below shows our stand at ICT 2013. Our team put enormous effort to make the robot versus human competition system, fine-tuned just for ICT 2013 purposes. Not only that both robots - ASCENS and human controlled - worked perfectly, the response of the audience has been rewarding. We are having many visitors, hundreds of questions and a lot of fun!

Nikola Serbedzija

Fraunhofer, Fokus

Formal Approach
The ability to represent knowledge providing for autonomic behavior is an important factor in dealing with uncertainty. In our approach, the autonomic self-adapting behavior is provided by policies, events, actions, situations, and relations between policies and situations (see Definitions 1 through 8). In our KR&R model Policies (Π) are responsible for the autonomic behavior. A policy π has a goal (g), policy situations (Si_π), policy-situation relations (R_π), and policy conditions (N_π) mapped to policy actions (A_π), where the evaluation of N_π may imply the evaluation of actions (denoted with N_π→A_π) (see Definition 2). A condition is a Boolean function over ontology (see Definition 4) or the occurrence of specific events or situations in the system. Thus, policy conditions may be expressed with policy events. Policy situations (Si_π) are situations (see Definition 6) that may trigger a policy π, which implies the evalua-tion of the policy conditions N_π (denoted with Si_π→π→N_π). A policy may also comprise optional policy-situation relations (R_π) justifying the relationships between a policy and the associated situations. The presence of probabilistic belief in those relations justifies the probability of policy execution, which may vary with time. A goal is a desirable transition from a state to another state (denoted with s⇒s') (see Definition 5). A situation is expressed with a state (s), a history of actions (A_si^←) (actions executed to get to state s), actions A_si that can be performed from state s and an optional history of events E_si^← that eventually occurred to get to state s (see Definition 7).

Ideally, policies are specified to handle specific situations, which may trigger the application of policies. A policy exhibits a behavior via actions generated in the environment or in the system itself. Specific conditions determine, which specific actions (among the actions associated with that policy – see Definition 2) shall be executed. These conditions are often generic and may differ from the situations triggering the policy. Thus, the behavior not only depends on the specific situations a policy is specified to handle, but also depends on additional conditions. Such conditions might be organized in a way allowing for synchroniza-tion of different situations on the same policy. When a policy is applied, it checks what particular conditions are met and performs the associated actions (see map(N_π,A_π) – see Definition 2). The cardinality for the policy-situation relationship is many-to-many, i.e., a situation might be associated with many policies and vice versa. Moreover, the set of policy situations (situations triggering a policy) is open-ended, i.e., new situations might be added or old might be removed from there by the system itself. With a set of policy-situation relations we may grant the system with an initial probabilistic belief (see Definition 2) that certain situations require specific policies to be applied. Runtime factors may change this probabilistic belief with time, so the most likely situations a policy is associated with can be changed. For example, the successful rate of actions execution associated with a specific situation and a policy may change such a probabilistic belief and place a specific policy higher in the "list" of associated policies, which will change the behavior of the system when a specific situation is to be handled. Note that situations are associated with a state (see Definition 7) and a policy has a goal (see Definition 2), which is considered as a transition from one state to another (see Definition 5). Hence, the policy-situation relations and the employed probabilistic beliefs may help a cognitive system what desired state to choose, based on past experience.

Case Study
To illustrate autonomic behavior based on this approach, let us suppose that we have a robot that carries items from point A to point B by using two possible routes - route one and route two (see Figure 1). A situation si1:“robot is in point A and loaded with items” will trigger a policy π1:“go to point B via route one” if the relation r(si1,π1) has the higher probabilistic belief rate (let’s assume that such a rate has been initially given to this relation because route one is shorter – see Figure 1.a). Any time when the robot gets into situation si1 it will continue applying the π1 policy until it gets into a situation si2:“route one is blocked” while applying that policy. The si2 situation will trigger a policy π2:“go back to si1 and then apply policy π3” (see Figure 1.b). Policy π3 is defined as π3:“go to point B via route two”. The unsuccessful application of policy π1 will decrease the probabilistic belief rate of relation r(si1,π1) and the eventual successful application of policy π3 will increase the probabilistic belief rate of relation r(si1,π3) (see Figure 1.b). Thus, if route one continues to be blocked in the future, the relation r(si1,π3) will get to have a higher probabilistic belief rate than the relation r(si1,π1) and the robot will change its behavior by choosing route two as a primary route (see Figure 1.c). Similarly, this situation can change in response to external stimuli, e.g., route two got blocked or a "route one is obstacle-free" message is received by the robot.

Figure 1: Self-adaptation Case Study