Computational Causal Behaviour Models (CCBM) are a modelling approach describing human behaviour in terms of precondition-effect rules, different action selection heuristics and probabilistic action durations. The CCBM toolkit allows to compile these models into various Bayesian filters such as Hidden Markov Models, Particle Filter or Marginal Filter. This allows for CCBM to reason about the user’s situation, actions, intentions, and causes behind the observed behaviour based on noisy observations.

Computational state space models (CSSMs) enable the knowledge-based construction of Bayesian filters for recognizing intentions and reconstructing activities of human protagonists in application domains such as smart environments, assisted living, or security. Computational, i. e., algorithmic, representations allow the construction of increasingly complex human behaviour models. Computational Causal Behaviour Models (CCBM) are a modelling approach that represent human behaviour computationally by means of preconditions and effects, different action selection heuristics and probabilistic action durations. The CCBM toolkit compiles these models into various Bayesian filters such as Hidden Markov Models, Particle Filter or Marginal Filter. The Marginal Filter is specifically tailored for categorical state spaces, as they are generated by CCBM. It has been shown to outperform the standard method for approximative Bayesian inference – the Particle Filter. This allows for CCBM to reason about the user’s situation, actions, intentions, and causes behind the observed behaviour based on noisy and ambiguous observations such as sensor data.

The dynamic Bayesian network used as probabilistic model in the CCBM approach is defined as follows: The hidden state X_t is a five-tuple (A_t, D_t, G_t, S_t, U_t). The action executed at time t is denoted by A_t, D_t is a boolean flag signalling whether A_t-1 is terminated in the left-open and right-closed interval (t-1, t] and a new action A_t has to be selected. U_t is the starting time of the action A_t. The variable St is the current environment state and Gt denotes the goal (or intention) of the user at time t. The observation Y_t=(W_t, Z_t) for time-step t consists of two conditional independent parts, the state observation Wt and the action observation Z_t. V_t is the timestamp of the observation data sequence.